Comparative Analysis of the Factors Influencing Metro Passenger Arrival Volumes in Wuhan, China, and Lagos, Nigeria: An Application of Association Rule Mining and Neural Network Models ()
1. Introduction
To create more sustainable and livable urban environments worldwide, policymakers must prioritize the transition of transportation development towards a more environmentally friendly future. This necessitates a comprehensive and coordinated approach to policy development and decision-making, to improve affordable, economically viable, people-centered, and environmentally sustainable transportation systems. Metro systems are critical in urban transportation, providing significant social and economic advantages. Enhancing service quality to meet passenger needs and ensure customer retention is crucial for the sustainable growth of metros. Currently, metro station evaluation systems mainly focus on planning and do not fully consider operational realities. An effective evaluation system should prioritize user experience, accurately assess operational quality and guide improvements accordingly. Thus, it is essential to identify the key factors that influence metro passenger arrival volumes to enhance service delivery [1]. Since the advent of metro rail transit, remarkable strides have been made in the realms of transportation and communication [2]. Electric trains, originally pioneered by London in 1890, have played a pivotal role in propelling technological, commercial, and socioeconomic progress over the years [3]. Furthermore, numerous developed nations, including Italy, France, Germany, Poland, the Netherlands, Spain, and Switzerland, have made substantial investments in metro and high-speed rail systems, yielding extensive benefits across diverse domains.
Urban metro systems have been implemented by numerous cities to tackle environmental and traffic challenges resulting from high-density urbanization [4] In China, metros are crucial underground public transit systems characterized by high capacity and dedicated rights-of-way, predominantly operating through underground tunnels [5]. In the past two decades, urban metro networks have expanded rapidly in China, with a total metro system length of 5180.6 km across 37 cities on the mainland by the end of 2019 [6]. In fact, four metro systems in China were among the top ten longest metro systems worldwide by the end of 2017 [7]. The swift development of metro systems in Chinese cities in recent years has resulted in substantial impacts on society, the economy, and the environment.
The Nigerian railway industry has been in a state of decline for many years, primarily due to inadequate funding and neglect. Since gaining independence in 1960, the railway system has seen minimal restructuring. This long-term neglect has led to a significant deterioration in both freight and passenger services, as well as rolling stock, drastically reducing the system’s capacity and functionality. With Nigeria being Africa’s most populous nation, with a current population of around 190 million and an expected increase to 260 million by 2030, the absence of reliable rail transport and freight services has contributed to a continual decline in socioeconomic development, decreased exports, elevated transportation costs, and increased strain on the road network, resulting in traffic congestion, accidents, and pollution [8].
As per the current global trends, major urban centers such as Lagos are developing efficient modern rail mass transit systems. The Lagos Metropolitan Area Transport Authority (LAMATA) has outlined plans for a comprehensive seven-line rail network spanning approximately 246 km to address the city’s long-term transportation needs. The completion of this network is projected by 2025. The initial phase encompasses two operational lines, namely the Red Line (Agbado to Marina) and the Blue Line (Okokomaiko to Marina). The Red Line, covering a distance of 31 kilometers, is designed to include a six-kilometer spur leading to the Murtala Muhammed International Airport. On the other hand, the Blue Line extends over 27 km. These lines converge at Iddo and traverse the lagoon to reach the Marina via a specially constructed suspension bridge [9]. With the successful establishment of the Red and Blue lines, long-term plans are taking shape for an additional five lines to complete the 246 km network by 2025 [10]. The Green Line is set to run eastward from Marina to Lekki airport, running parallel to the coastline. Conversely, the Yellow Line diverges from the Blue Line at the National Theatre near Iddo and proceeds northwest to Otta in Ogun State. A short branch from the Red Line at Oshodi will cater to the international and domestic terminals at Murtala Mohammed International Airport. The Brown and Orange lines will cater to the northeast, sharing the Red Line’s tracks from Marina to Jibowu before heading to another junction at Ojota. The Brown Line is slated to terminate at Mile 12, while the Orange Line will continue its route north across the Long Bridge to Redeem Camp in the satellite township of Mowe/Ibafo. Lastly, the Purple Line will provide an orbital route from Ojo in the west to the Lagos-Ibadan Expressway Toll gate in the northeast, where it will link up with the Orange Line tracks to reach Redeem. Interchanges have also been indicated by Yellow and Red lines in the northern suburbs. Additionally, a monorail encircling Lagos Island will serve as the city center [10]. Understanding how weather, time of day, waiting time, travel behavior, arrival pattern and metro satisfaction impact passenger usage is crucial, given the ambitious plans of the Lagos Metropolitan Area Transport Authority (LAMATA) to develop a comprehensive rail network covering about 246 km. The completion of the Red and Blue lines, as well as the proposed expansion to include the Green, Yellow, Brown, Orange, and Purple lines by 2025, highlights the importance of studying these factors [11] [12]. However, providing high-quality service in public transportation is essential to attract more passengers [13]. This study thoroughly examines the factors that affect passenger arrival volumes at metro stations, focusing on Yujiatou Station along Wuhan Metro Line 5 in China and the Lagos Light Rail Blue Line in Nigeria. It emphasizes the significant impact of weather conditions, time of day, waiting time, travel behavior, arrival patterns, and metro satisfaction. The analysis is based on a comprehensive review of existing literature that addresses the various influences on rail transit passenger volumes, highlighting the need for a thorough and detailed approach to urban transit planning and improving passenger satisfaction. Scholars’ dedication to thoroughly assessing the multitude of factors influencing rail transit passenger volumes emphasizes the necessity for a comprehensive strategy in transit planning. By exploring the interaction between temporal variations, weather conditions, waiting times, travel patterns, arrival patterns, and satisfaction levels, this study aims to improve metro services. Understanding and addressing these influencing factors is crucial, given that efficient metro transit systems are characterized by high service frequency. The literature review provided here sets the stage for an in-depth analysis of passenger arrival volumes, their determinants, and the critical assessment of passenger waiting times, which directly correlates with overall arrival volumes. This research will provide insights and enhance metro planning methods, leading to a more efficient and passenger-focused transit service.
2. Literature Review
2.1. Temporal and Meteorological Impacts on Metro Passenger Arrival Volume
According to recent studies, metro systems are impacted by various weather conditions, resulting in both positive and negative effects. For example, warmer weather typically leads to increased ridership, while cold and windy conditions tend to decrease transit use [14]. The impact of weather on public transportation varies widely across different systems. A study analyzing daily metro ridership in Nanjing from 2011 to 2014 found that certain transportation modes are more resilient to adverse weather than others. It was also noted that weekend travelers tend to be more affected by weather conditions compared to weekday passengers [15]. Additionally, the influence of weather on travel behavior depends on the mode of transportation and its perceived comfort. Extreme weather affects different modes differently, with private cars often seen as more comfortable and reliable during warmer conditions, potentially reducing subway usage. However, subways generally maintain steady ridership due to commuters’ adaptability in switching between transportation modes [16]. Weather fluctuations can also impact metro operations, leading to reliability issues and increased operational costs [17]. The exacerbation of these challenges by climate change is noted, as severe weather events impact leisure travel more than daily commuting. For instance, in New York City, rain and snowfall typically decrease public transport usage, while lower-than-normal temperatures may have a positive effect [18]. Notably, based on hourly and station-level data, weather was observed to have the most significant impact on passenger volume in the afternoon, followed by midday and morning [19]. Moreover, the time of day significantly influences passenger arrival volumes, categorizing travelers into schedule-dependent and independent groups. This distinction is evident across morning, evening, and off-peak periods, affecting how passengers choose their travel times based on metro schedules [20]. The study also revealed that passenger behavior and arrival patterns vary accordingly, with peak hours typically experiencing higher volumes compared to off-peak times [21]. Therefore, to comprehensively understand and predict passenger arrival trends throughout the day, it’s essential to analyze these variations across different timeframes.
2.2. Metro Passenger Travel Behavior and Arrival Patterns
As per the findings of Jolliffe et al. [22]-[24], the frequency of train services is a crucial factor in shaping passenger arrival patterns. The availability of gaps in public transportation significantly affects these patterns. The impact of train frequency on passenger arrivals is manifested in various ways. Higher frequencies result in less predictable arrival patterns, leading to an average waiting time of approximately half the headway. Conversely, lower frequencies prompt passengers to strategically time their arrivals to minimize waits [25]. Research has indicated that the shift from random to non-random arrival patterns typically occurs within headways ranging from 5 to 11 minutes, regardless of frequency levels. Passenger behavior also plays a significant role in shaping arrival patterns, with individuals categorized into those adhering to schedules and arriving on time, and those arriving randomly [26].
The unpredictability and fluctuation of passenger arrivals over time can significantly impact operational efficiency, particularly during peak hours when arrival flows vary dynamically. Addressing this uncertainty often involves assuming probability distributions for arrival volumes, although accurately defining such distributions can be challenging in practical applications. Therefore, considering a range of variability in arrival volumes is often deemed more practical than relying solely on precise probability distributions [27].
2.3. Metro Passengers Waiting Time
Recent studies highlight the challenges faced by commuters in major urban areas due to extended wait times and limited train capacity in crowded train stations [28]. The average waiting times for passengers vary based on factors such as station congestion, time of day, and trip purpose. The average waiting times for passengers vary based on factors such as station congestion, time of day, and trip purpose. During peak hours, passengers typically experience shorter wait times while random arrivals are more common during off-peak periods [20] [27]. Overcrowding can lead to longer waits at the original station as passengers vie for space on the train [29]. Furthermore, waiting time is a critical aspect of the passenger experience, significantly influencing behavior and potentially causing feelings of anxiety and dissatisfaction [30]. To address this issue, recent technological advancements have revolutionized the estimation of passenger waiting times. Smart card data has proven highly effective in collecting trip information at entry and exit points within the metro system, minimizing the need for manual data collection [31].
Conversely, passengers who refer to schedules with varying arrival frequencies aim to minimize wait times by arriving close to departure times. Previous studies modeling passenger waiting times often assumed random arrivals and calculated average waiting times by multiplying the average bus headway by twice the ratio of the average headway to headway variance [32]. Initial investigations into estimating rail transportation wait times were influenced by research in bus transportation. Researchers explored models assuming uniformly distributed wait times using random passenger arrival models, advancing their studies in this area [33] [34]. A study conducted in Zurich, Switzerland, focused on measuring wait times for buses, trams, and trains at stations, recommending the use of a mixed uniform and Johnson SB distribution for modeling purposes [20]. Another study examining waiting times for both regional and metro lines in Copenhagen considered headway ranges from 2 to 60 minutes [35].
2.4. Metro Schedule and Headway Optimization
Generally, the interval between train arrivals plays a crucial role in identifying potential conflicts during train headway and timetable optimization. Optimizing the metro system timetable is widely acknowledged as a traditional decision-making challenge that requires balancing the needs of passengers and the metro operating company [36]. Numerous research initiatives have aimed to enhance the functionality and productivity of public transportation systems. The metro timetable, closely linked to the train schedule, details arrival and departure times along with stop durations at each station. To enhance public transportation efficiency during peak hours, Beijing Metro Lines 1 and 2 have significantly reduced headway times to just two minutes. Consequently, it is imperative for operated trains to maintain consistent service frequency and adhere strictly to predetermine arrival and departure times specified in the schedule [37]. Moreover, fixed headway train timetables are inadequate for handling variable demand due to significant fluctuations in transportation needs [38]. To tackle this issue, a mixed-integer linear programming approach has been proposed [39]. Furthermore, a stochastic programming model has been proposed and refined for metro train rescheduling as a decision-making method to address railway routing and scheduling challenges [40]. Additionally, most train timetable optimization models consider the minimum headway between consecutive trains as a fixed value, although they also allow this minimum headway to be influenced by the current track assignment conditions [41]. Moreover, a mixed-integer linear programming model has been developed to optimize train schedules and reduce passenger waiting time disparities [42].
2.5. Passenger Satisfaction in Metro Systems
The satisfaction and emotional responses of travelers are shaped by their perceptions and expectations of their journey, which are influenced by subjective feelings and views of various aspects of the travel experience [43]. Currently, there is no systematic method in place for evaluating passenger satisfaction at metro stations. Previous research has explored several factors contributing to passenger satisfaction, such as accessibility, information availability, time efficiency, customer service, comfort, safety, convenience, reliability, cost-effectiveness, and capacity considerations [44]. During the evaluation process, researchers typically choose indicators based on existing literature or personal experiences. However, these indicators can be subjective and may not accurately capture the evolving dynamics of metro services. Furthermore, opinions regarding the use of these indicators can vary depending on location, objectives, and time periods. Therefore, it is essential to carefully review and adjust evaluation criteria to align with specific research goals. Assessing passenger satisfaction with public transportation services is crucial for both transportation research and practical applications. To improve infrastructure, amenities, services, and increase public transport usage, transit agencies must understand how well they meet passenger expectations. Conducting customer surveys is critical as they provide valuable insights to transit agencies about aspects significant to passengers and specific areas of satisfaction or dissatisfaction [45]. In a study focused on Metro Rail Transit 3 (MRT3) stations in Metro Manila, Philippines, [46] identified reasons for low ridership from accessibility and inter-modality perspectives. The main sources of passenger dissatisfaction include station congestion, relatively high fares, and inconveniences in connecting transport facilities to other modes of transit. The remainder of this paper is organized as follows: Section 2 details the materials and method used. In Section 3, we present the main results of the study. Section 4 discusses these results and future research directions. Finally, Section 5 provide the conclusion.
3. Materials and Methods
This study employs a comprehensive methodological framework to compare subway passenger arrival volumes between the Wuhan Metro and the Nigeria Blue Line. This comparison accounts for the disparities in time granularities and the scopes of the respective studies. Data was collected from hourly station-specific entries in Wuhan and daily line-level volumes in Lagos. The Lagos data was normalized by estimating hourly arrival volumes through consistent daily traffic assumptions to enable a meaningful comparison. Meanwhile, proportional analysis focused on relative changes in passenger arrival volume patterns acquired from station-specific hourly entries within Wuhan and daily volume metrics on a line-level basis in Lagos. To facilitate an analytical comparison, the data about Lagos were subjected to normalization by deducing hourly arrival volumes via assumptions of consistent daily traffic flows. This approach enabled a detailed examination of relative alterations in passenger arrival volume patterns, employing proportional analysis methodologies. Given the different scopes of station-specific data for Wuhan and line-level data for Lagos, contextual comparisons were made by focusing on crucial transit hubs in Wuhan and treating the entire line as a single unit of analysis in Lagos. The study compared these contexts by identifying peak hours in Wuhan and contrasting them with the busiest periods in Lagos. It also analyzed the proportional distribution of passengers across different times of the day in Wuhan and compared it with Lagos’s daily flow. To summarize the total passenger arrival volumes for the Lagos Light Rail Blue Line, we analyzed daily data covering weekdays and weekends. From the daily records of passenger counts, the total weekday morning peak was 4713 passengers, while the weekend morning peak was 378. Weekdays had an average of 2485 passengers for the off-peak periods, and weekends had 216.5 passengers. During the evening peak, weekdays saw 3770 passengers, and weekends had 303 passengers.
However, the dataset utilized in this research was obtained from a questionnaire distributed to respondents in Wuhan, China, and Lagos, Nigeria. Additionally, it included manual data from Yujiatou station and two other stations, as well as online data, specifically the Lagos Light Rail Blue Line Passenger arrival volume Statistics. The Wuhan questionnaire was initially drafted in English, translated into Chinese, and uploaded as a Wenjuanxing Form. Conversely, the Lagos questionnaire was created and uploaded in English as a Google form, with hyperlinks and QR codes generated for both. These questionnaires were then emailed to randomly selected respondents in both cities via social media and emails. The data were collected between May 2024 and July 2024, yielding 365 valid responses for the Lagos questionnaire and 403 for the Wuhan questionnaire.
The questionnaire gathered information on various variables, including weather, time of day, waiting time, arrival pattern, travel behavior, and metro satisfaction. The manual collection was conducted at Yujiatou Station and two other stations over six weeks to obtain the necessary data. Data were collected during both peak and off-peak hours to provide a comprehensive understanding of passenger behavior. A total of 24 questions were designed to cover all the essential variables of the study, such as passenger arrival volume, time of day, weather conditions, waiting time, travel behavior, arrival pattern, and metro satisfaction. The questionnaire aimed to gather comprehensive and meaningful data by incorporating questions targeting these variables. The questions included:
1) Demographic, gender, age, and level of education (Questions 1 - 3)
2) Metro passenger arrival volume (Questions 4 - 6)
3) Weather-related aspects (Questions 7 - 9)
4) Time of day-related aspects (Questions 10 - 12)
5) Waiting time-related aspects (Questions 13 - 15)
6) Metro satisfaction (Questions 16 - 18), which asked:
Whether satisfactory service leads to higher passenger arrival volumes
Whether satisfaction with the metro influences the decision to use it
Whether overall satisfaction with the metro positively affects the number of passengers
7) Travel behavior (Questions 19 - 21), which sought to determine:
How frequently do participants use the metro for commuting to work, social events, or school/university?
To what extent do participants agree that their travel behavior, such as taking alternative modes of transportation or adjusting travel time, influences the rate of passenger arrivals at the metro station?
8) Arrival patterns (Questions 22 - 24), which asked:
To what extent do participants agree that the pattern of passenger arrivals influences the overall passenger volume at the metro station?
How important is understanding the passenger arrival pattern when planning a trip to the metro station?
Whether participants observe a consistent pattern in the arrival of passengers at metro stations throughout the day?
As presented in the Appendix C.
Additionally, the following steps were taken to ensure the accuracy of the subsequent analyses: the collected data were cleaned to remove inconsistencies or missing values. Additionally, data preprocessing involved normalizing the data and encoding categorical variables as needed. However, to evaluate the internal consistency of the questionnaire, we calculated Cronbach’s alpha, which is a widely used measure of reliability. Table 1 below shows the internal consistency of the dependent variable passenger arrival volume is 0.817, and independent variables such as weather is 0.834, time of day is 0.826, waiting time as 0.811, metro satisfaction as 0.808, travel behaviour as 0.837 and arrival pattern as 0.842 which indicates high internal consistency, exceeding the recommended threshold of 0.7.
Table 1. Reliability analysis results.
Variable |
Item |
Scale Mean if Item Deleted |
Scale Variance if Item Deleted |
Corrected Item-Total Correlation |
Cronbach’s Alpha if Item Deleted |
Passenger Arrival volume |
PAR1 |
50.48 |
92.192 |
0.735 |
0.808 |
PAR2 |
50.64 |
97.169 |
0.392 |
0.825 |
PAR3 |
50.66 |
94.276 |
0.511 |
0.818 |
Weather |
WTH1 |
50.66 |
98.862 |
0.343 |
0.828 |
WTH2 |
50.78 |
97.719 |
0.331 |
0.829 |
WTH3 |
52.48 |
110.132 |
-0.170 |
0.846 |
Time of day |
TOD1 |
52.56 |
108.038 |
-0.044 |
0.841 |
TOD2 |
52.33 |
107.579 |
-0.015 |
0.840 |
TOD3 |
50.75 |
87.197 |
0.809 |
0.799 |
Waiting time |
WAT1 |
50.92 |
93.279 |
0.527 |
0.817 |
WAT2 |
50.94 |
90.321 |
0.644 |
0.810 |
WAT3 |
50.83 |
89.523 |
0.674 |
0.808 |
Metro satisfaction |
MS1 |
50.99 |
89.980 |
0.638 |
0.810 |
MS2 |
50.88 |
88.142 |
0.719 |
0.804 |
MS3 |
50.91 |
90.470 |
0.640 |
0.810 |
Travel behaviour |
TB |
51.26 |
100.956 |
0.203 |
0.837 |
Arrival pattern |
AP |
51.73 |
106.380 |
0.021 |
0.842 |
3.1. Model Selection
3.1.1. Association Rule Mining
The Apriori algorithm is a data mining technique used to discover patterns and associations within a dataset. It identifies frequent item sets and generates association rules based on these item sets. The algorithm evaluates the strength and relevance of the generated association rules using support, confidence, and lift measurements. The algorithm operates by incrementally enlarging the item sets until no additional frequent item sets can be discovered. It uses the following measures of significance and interest:
Support (S(x)): The proportion of responses in the dataset containing the item set.
Confidence: The likelihood of the rule being true.
Lift: The ratio of the observed support to the expected support if the two items were independent. This technique has been effectively used in studies by [47] [48]. Association rule mining is a well-established method employed to uncover relationships among variables within a large dataset, offering flexibility and not requiring a dependent variable. In this study, the Apriori algorithm was selected for its flexibility [49]. The arules package in R was used for analysis.
The specifics of the algorithm are as follows: Let
represent the set of factors influencing metro passenger arrival volume referred to as the item set, and
denote the set of responses from individual respondents, known as the data set; every response in D possesses a unique identifier and includes a subset of the items in I. A rule of an item set is expressed as {X} ⇒ {Y} where:
and
(X and Y are disjoint items). The sets of items X are called antecedent (or Left-hand side LHS) and sets of items Y consequent (or Right-hand side RHS) of the rules. There are mainly three measures of significance and interest which are the support, confidence, and lift. The support S(x) of an item set is the proportion of responses in the dataset which contains the item set given as:
(1)
(2)
(3)
where:
f(x) = Number of Instances with x
f(y) = Number of Instances with y
N = Total number of Instances
= Number of Instances with both x and y
S(x) = Support of x item set
S(y) = Support of y item set
= Support of the association
The confidence is an estimate of probability P(xy) of finding Consequent (RHS) of the rule in instances under the condition that these instances also contain the antecedent (LHS). The confidence is given as:
(4)
The Lift is the deviation of the support of the whole rule from the support expected under independence given the support of LHS and RHS, with a greater value indicating a better association.
(5)
The Apriori algorithm, implemented using the open-source R programming language and the “arules” and “arulesviz” packages, was employed to investigate the relationship between high, moderate and low metro passenger arrival, with other factors such as waiting time, weather, and time of day, arrival pattern, travel behaviour and metro satisfaction.
3.1.2. Neural Network Models
Neural network models were deployed to detect nonlinear patterns in the data. The neural network architecture featured multiple hidden layers, and the training process incorporated backpropagation and the Adam optimizer. The effectiveness of the neural network models was assessed using metrics such as accuracy, precision, recall, and F1-score. The input data is transmitted to the hidden layers for processing, and the final hidden layer forwards the processed information to the output layer and receives the outcomes. This investigation employs a fully connected neural network, where each neuron in one layer is connected sequentially to every neuron in the subsequent layers, encompassing the input, hidden, and output layers. This approach is consistent with studies by [50] [51]. The process of deriving output data is delineated by the following equation:
(6)
where,
X is output of unit k in the nth layer, f is the function of activation,
is the input vector,
is a weight vector,
is the bias weight.
The initial weights are typically assigned randomly at the beginning of neural network training, which involves adjusting these weights through backpropagation comprises two main phases: feedforward and backward propagation. A training set, consisting of input vectors and corresponding target output vectors, is provided to the network for learning. The network’s actual output is compared with the target output to calculate an error, which is then used to update the weights by propagating them. Iterative weight adjustments are performed for each training set until a stopping condition, such as a predefined number of epochs or a specified threshold, is met. The backpropagation algorithm consists of three key stages:
1) Feedforward Stage: The input layer computes the output by summing the weighted inputs and biases up to the output layer using a specified activation function.
2) Backpropagation stage: The error, obtained by comparing the network output with the target output, is calculated and propagated backward through the network starting from the output layer.
3) Weight and Bias Update Stage: In this final phase, the weights are adjusted to minimize errors based on the back propagated error signals.
3.1.3. Comparison of Models
Evaluation metrics were used to compare the performances of linear regression, neural network models, and association rule mining to determine the most effective approach for predicting passenger arrival. The complete methodology process is presented in Figure 1 below.
Figure 1. Research methodology framework.
3.1.4. Abbreviations and Acronyms
Considering your recent experiences how would you rate the overall volume of passengers arriving at metro stations? |
PAR1 |
During the busiest times of the day, how would you rate the level of crowding on the metro? |
PAR2 |
Comparing to a year ago, how would you describe the change in metro passenger arrival volume? |
PAR3 |
Weather conditions (e.g. rain, snow, heat) impact metro passenger arrival volumes |
W1 |
The metro passenger arrival volume significantly increases during peak summer/winter. |
W2 |
What type of weather-related disruptions are most likely to stop you from coming to the metro station? |
W3 |
How likely are you to use alternative transportation or adjust your travel schedule due to congestion or delays during peak hours? |
TOD1 |
How would you rate the difference in metro passenger arrivals between peak and off-peak hours? |
TOD2 |
I perceive a significant increase in metro usage during evening peak hours. |
TOD3 |
Longer waiting times at metro stations result in more people using the metro. |
WAT1 |
Shorter waiting times lead to higher passenger arrival volumes at metro stations. |
WAT2 |
Perceived waiting time influences the number of passengers arriving at metro stations. |
WAT3 |
Satisfactory metro service leads to higher passenger arrival volumes. |
MSS1 |
I am more likely to use the metro when satisfied with its service. |
MSS2 |
Overall satisfaction with the metro positively influences the number of passengers using it. |
MSS3 |
How often do you change your travel plans in response to real-time information about metro passenger volume or congestion, which may impact passenger arrival volumes? |
TB1 |
To what extent do you agree that your travel behaviour, such as taking alternative modes of transportation or adjusting your travel time influences the rate of passenger arrivals at metro station? |
TB2 |
How frequently do you use the metro for commuting to work, social events, or school/university? |
TB3 |
To what extent do you agree that the pattern of passenger arrivals influence the overall passenger volume at the metro station? |
AP1 |
How important is understanding the passenger arrival pattern to you when planning your trip to the metro station? |
AP2 |
Do you observe a consistent pattern in the arrival of passengers at metro stations throughout the day? |
AP3 |
4. Results
4.1. Direct Field Observation of Metro Passengers Arrival Volume
During a period of six weeks, we collected passenger arrival volume data at Yujiatou, Qingnian Road, and Jiangshe 2nd Road stations, focusing on both weekday and weekend passenger arrival volume during morning peak hours, off-peak hours, and evening peak hours. The data reveals some significant patterns. You can find the summary of this data in the provided Table 2 and Figure 2, Figure 3 and Figure 4 below. For a comprehensive view of the entire dataset, which includes all stations and specific time intervals, please refer to the Appendix A.
Table 2. Summary of metro passenger arrival volume observations.
Station |
Time Period |
Highest Passenger Count |
Lowest Passenger Count |
Remarks |
Yujiatou |
Morning Peak (Week 1) |
1754 (Monday) |
407 (Sunday) |
Highest on Monday, sharp drop on weekends. |
|
Evening Peak (Week 4) |
1257 (Friday) |
653 (Sunday) |
Friday peak, with gradual decline toward Sunday. |
Qingnian Road |
Morning Peak (Week 5) |
1670 (Monday, Friday) |
490 (Sunday) |
Consistent high on weekdays, lowest on Sunday. |
|
Evening Peak (Week 5) |
2041 (Friday) |
1134 (Saturday) |
Highest passenger arrival volume on Friday evening. |
Jiangshe 2nd Rd |
Morning Peak (Week 6) |
1131 (Monday) |
613 (Sunday) |
Higher weekday traffic, Sunday low. |
|
Off-Peak (Week 6) |
624 (Saturday) |
555 (Wednesday) |
Off-peak hours show relatively consistent traffic. |
The pie charts in Figure 2 show that weekday passenger volumes are consistently higher than weekends, reflecting typical commuter patterns. During summer holidays, more off-peak hour travel on weekends indicates increased leisure travel. This highlights the impact of summer holidays on metro travel patterns, with heightened off-peak and weekend travel volumes due to non-commuter activities.
Figure 2. Weekdays & weekends metro passenger arrival volume for both week 1 & week 2.
Figure 3. Weekdays & weekends metro passenger arrival volume for both week 3 & week 4.
Figure 3 Shows that In July 2023, there was increased off-peak hour and weekend travel, indicating more leisure travel due to school closures and vacations. In May 2024, there were dominant weekday morning peak hours and increased evening peak hour travel, suggesting more evening activities or later commutes.
Figure 4. Weekdays & weekends metro passenger arrival volume for both week 5 & week 6.
Figure 4 above shows typical weekdays at the Qingnian Road Station. The higher percentage of evening peak hour dominance suggests that many commuters used this station to return home from work or school. In contrast, the lower off-peak percentage indicates that the station primarily serves as a hub for commuting, with less leisure or non-work-related travel occurring during the middle of the day. Compared to the Qingnian Road Station, the Jiangshe 2nd Road Station I had a noticeable increase in off-peak travel during weekends. A higher off-peak percentage on weekends indicates more leisure travel, with individuals using the station for activities such as shopping, dining, or visiting friends and family. However, evening peak hours remained dominant, suggesting significant evening activity.
4.2. Lagos Blue Line Passenger arrival volume
Table 3 and Figure 5, Figure 6 and Figure 7 below, shows a summarized information on significant fluctuations in average daily passenger arrival volume over several months, with peaks near 9000 passengers and troughs around 3000. Weekdays have a much higher average daily passenger arrival volume (approximately 5886 passengers) compared to weekends (around 1765 passengers), indicating the dominance of work and school commutes. Passengers flow is relatively balanced between mornings and evening on weekends, reflecting more flexible travel patterns. The detailed information is in the Appendix B.
Table 3. Summary of the Lagos Light Rail Blue Line Passenger arrival volume.
Metric |
Value |
Total Days Recorded |
200 |
Total Passenger arrival volume |
778,422 |
Average Daily Passenger arrival volume |
3892.11 |
Max Daily Passenger arrival volume |
10,901 (11/3/2023) |
Min Daily Passenger arrival volume |
0 (10/15/2023) |
Max Daily Train Trips |
54 (from 10/16/2023 onward) |
Min Daily Train Trips |
10 (9/4/2023) |
Total Train Trips Recorded |
8812 |
Average Daily Train Trips |
44.06 |
Max Daily Revenue (Confidential) |
Confidential |
Min Daily Revenue (Confidential) |
Confidential |
Highest Passenger arrival volume on a Weekday |
10,901 (11/3/2023) |
Lowest Passenger arrival volume on a Weekday |
343 (9/10/2023) |
Highest Passenger arrival volume on a Weekend |
6440 (11/4/2023) |
Lowest Passenger arrival volume on a Weekend |
159 (2/11/2024) |
Source: LAMATA Lagos Nigeria.
Figure 5. Average daily passenger arrival volume over time in lagos blue line.
Figure 6. Average daily passenger arrival volume by day of the week in lagos blue line.
Figure 7. Estimated average metro passenger arrival volume in lagos blue line.
Figure 5 and Figure 6 show significant fluctuations in average daily passenger arrival volume over several months, with peaks nearing 9000 passengers and troughs around 3000. Weekdays have a much higher average daily passenger arrival volume (approximately 5886 passengers) than weekends (around 1765 passengers). Figure 7 reinforces this, showing a more significant portion of weekday passenger arrival volume. This emphasizes the need for optimized metro services to accommodate higher weekday demand while maintaining balanced weekend operations.
4.3. Association Rule Mining Technique
The Apriori algorithm analyzed metro passenger arrival volumes, generating 420 association rules grouped into high, moderate, and low classes. Strong correlations were found between high passenger volumes and longer waiting times, increased evening peak usage, high metro satisfaction, shorter waiting times, perceived waiting times, and weather conditions. Moderate volumes had significant associations with longer waiting times, disagreement on waiting times, neutral stance on perceived waiting times, and disagreement on weather conditions affecting arrival volumes. Low volumes strongly disagreed with perceived waiting times, shorter waiting times, overall metro satisfaction, increased evening peak usage, and weather conditions, all of which were strongly linked to low passenger volumes. These results highlight the impact of waiting times, peak periods, satisfaction levels, and weather conditions on metro passenger volumes. As presented in Table 4 and Figure 8, Figure 9 and Figure 10 below.
Table 4. Association rule mining result for PAR1.
|
PAR1 |
|
Support |
Confidence |
Lift |
Considering your recent experiences, how would you rate the overall volume of passengers arriving at metro stations? |
High |
{WAT1 = Strongly Disagree} |
0.246 |
0.346 |
1.369 |
{TOD3 = Strongly Agree} |
0.315 |
0.442 |
1.363 |
{MSS3 = Strongly Agree} |
0.249 |
0.35 |
1.352 |
{WAT2 = Strongly Agree} |
0.239 |
0.336 |
1.350 |
{WAT3 = Strongly Agree} |
0.292 |
0.410 |
1.345 |
{WTH2 = Strongly Agree} |
0.295 |
0.415 |
1.332 |
Moderate |
{MSS1 = Neutral} |
0.095 |
0.518 |
2.589 |
{WAT1 = Disagree} |
0.046 |
0.25 |
2.061 |
{WAT3 = Neutral} |
0.082 |
0.446 |
2.032 |
{WTH1 = Disagree} |
0.046 |
0.25 |
2.007 |
{WAT2 = Neutral} |
0.082 |
0.446 |
2.002 |
Low |
{WAT3 = Strongly Disagree} |
0.052 |
0.5 |
5.259 |
{WAT2 = Strongly Disagree} |
0.056 |
0.531 |
4.629 |
{MSS3 = Strongly Disagree} |
0.049 |
0.469 |
4.205 |
{TOD3 = Strongly Disagree} |
0.033 |
0.313 |
4.144 |
|
{WTH1 = Strongly Disagree} |
0.016 |
0.156 |
3.971 |
Figure 8. PAR 1 High.
Figure 8 above shows a strong correlation between passenger volumes and factors such as waiting times, weather impact, and peak hour usage at metro stations. It uses color intensity to represent the strength of these associations visually. Darker colors indicate stronger connections, while lighter colors indicate weaker ones. This visualization helps identify the factors most strongly correlated with high passenger volumes at metro stations.
Figure 9. PAR 1 Moderate.
From Figure 9 above, the visualization highlights that waiting times (both agreement and neutral stances) and weather conditions (neutral and disagreement stances) are significant factors that influence moderate passenger volumes at metro stations.
Figure 10. PAR 1 Low.
The figure highlights that the strongest correlations with low passenger volumes are between “WAT2 = Strongly Disagree” and “WAT3 = Strongly Disagree,” indicated by the highest lift values. Moderate correlations are seen in relationships such as “WTH1 = Strongly Disagree” and “MSS2 = Strongly Disagree.” Meanwhile, “WTH2 = Strongly Disagree” and “WTH3 = Extreme heat” show weaker, still significant, associations with lower lift values.
4.4. Descriptive Statistics
In the forthcoming analysis, Table 5 provides a comprehensive summary of the socio-demographic data obtained for this study. Three hundred sixty-five valid responses were collected from participants in Lagos, Nigeria. Among the respondents, 48.2% were male and 51.8% were female. The age distribution of the participants is as follows: under 18 years (19.2%), 18 years old (28.2%), 18 - 25 years (18.9%), 26 - 35 years (24.7%), and 36 - 45 years (9.0%). Regarding educational background, 46.8% of the participants are high school students, 36.4% are bachelor’s students, 11.5% are master’s students, and the remaining 5.2% are pursuing doctoral degrees. Additionally, 403 valid responses were obtained from participants in Wuhan, China. Among them, 48.4% were male and 51.6% were female. The age distribution in this group is as follows: under 18 years (18.9%), 18 years old (27.3%), 18 - 25 years (19.6%), 26 - 35 years (25.6%), and 36 - 45 years (8.7%). Regarding educational status, 47.6% of the participants are high school students, 30.0% are pursuing bachelor’s degrees, 11.4% are master’s students, and 5.0% are working towards doctoral degrees.
Table 5. Demographic distribution.
Variable Code Description |
N |
Percentage % |
N |
Percentage % |
Lagos |
Wuhan |
Demographic Distribution |
Gender |
MALE FEMALE |
176 |
48.2 |
195 |
48.4 |
|
189 |
51.8 |
208 |
51.6 |
|
Age |
under18 |
70 |
19.2 |
76 |
18.9 |
|
18 |
103 |
28.2 |
110 |
27.3 |
|
18 - 25 |
69 |
18.9 |
79 |
19.6 |
|
26 - 35 |
90 |
24.7 |
103 |
25.6 |
|
36 - 45 |
33 |
9.0 |
35 |
8.7 |
|
Educational level |
high school or below |
171 |
46.8 |
192 |
47.6 |
|
bachelor |
133 |
36.4 |
145 |
30.0 |
|
master |
42 |
11.5 |
46 |
11.4 |
|
doctorate |
19 |
5.2 |
20 |
5.0 |
4.5. Neural Network Model for Lagos Data Set
Neural networks are a type of machine learning model that closely resembles the human brain in structure and function. They excel at processing complex patterns and large amounts of data, making them particularly well-suited for tasks such as image recognition, natural language processing, and predictive analytics.
4.5.1. Regression Metrics
A lower MSE indicates superior model performance because it signifies that the predictions are closely aligned with the actual values. In this instance, an MSE of 0.1493 suggests that the model’s predictions are fairly accurate, although there is still potential for enhancement. R-squared values range from 0 to 1, with higher values denoting better model performance. An R-squared value of 0.8666 implies that approximately 86.66% of the variation in the dependent variable (Passenger Arrival) can be accounted for by the model. This suggests a robust association between the predictors and response variables. As presented in the Table 6 below.
Table 6. Regression metric results.
Metric |
Value |
Mean Squared Error |
0.1493 |
R-squared |
0.8666 |
4.5.2. Feature Importance
In this instance, feature importance is calculated using a method that involves shuffling the values of each feature and then measuring the increase in the model’s error. The greater the error increase, the more significant the feature. The results indicate that waiting time was the most crucial feature in this model, with an importance score of 7.0581, signifying its substantial contribution to prediction accuracy, followed by MetroSatisfaction, weather conditions, time of day, ArrivalPattern, and general travel behavior. As presented in Table 7 and Figure 11 below.
Table 7. Feature importance results.
Feature |
Importance |
Weather |
1.0316 |
Timeofday |
0.8904 |
WaitingTime |
7.0581 |
ArrivalPattern |
0.8571 |
TravelBehaviour |
0.6643 |
MetroSatisfaction |
6.1193 |
Figure 11. Feature importance in neural network.
The aforementioned Figure 11 illustrates the relationship between various features and passenger arrivals. Among these, Waiting Time is the most influential factor, implying that reducing waiting time can substantially enhance passenger arrival. Similarly, Metro Satisfaction has a high level of influence, suggesting that improving overall satisfaction can have a positive effect on passenger arrival. The impact of the weather was considered moderate, highlighting the importance of implementing weather adaptation strategies. The time of day also had a moderate effect, emphasizing the significance of optimizing operations based on time-of-day data. Finally, arrival patterns and travel behavior have a moderate influence, indicating the need for further analysis and a deeper understanding of these factors.
4.5.3. Model Prediction
The model’s predictions were impressively accurate, with minimal residuals for each prediction. The model consistently demonstrated a balanced mix of positive and negative residuals, indicating well-calibrated performance. Additionally, there were no discernible patterns in the residuals, suggesting that the model effectively captured the underlying data patterns without overfitting or under fitting. As presented in Table 8 and Figure 12 and Figure 13 below.
Table 8. Predicted and residual results.
Predicted |
Residual |
3.9926 |
0.0074 |
4.9648 |
0.0352 |
2.9851 |
0.0149 |
1.3190 |
0.0143 |
4.0079 |
-0.0079 |
2.3303 |
0.0030 |
Figure 12. Predicted and residual values.
Figure 13. Predicted vs residual values.
The predicted value plot above shows the target variable’s forecasted values and the residual plot displays variances between actual and predicted values. The scatter plot indicates that the residuals are randomly distributed without any systematic pattern, confirming the model’s high accuracy and absence of significant biases.
4.6. Neural Network Model for Wuhan Data Set
Neural networks are a type of machine learning model that closely resembles the human brain in structure and function. They excel at processing complex patterns and large amounts of data, making them particularly well-suited for tasks such as image recognition, natural language processing, and predictive analytics.
4.6.1. Regression Metrics
The regression metric results provide an evaluation of the model’s performance. The Root Mean Squared Error (RMSE) is 0.1970, indicating the average magnitude of the prediction errors, with lower values suggesting better model performance. The Mean Absolute Error (MAE) is 0.0921, representing the average absolute difference between the predicted and actual values, with smaller values indicating more accurate predictions. The R-squared value is 0.5615, which means that approximately 56.15% of the variance in the target variable is explained by the model. This suggests a moderate level of explanatory power, indicating that while the model captures some of the variability in the data, there is still room for improvement. As presented in Table 9 below.
Table 9. Regression metric results.
Metric |
Value |
RMSE |
0.1970 |
MAE |
0.0921 |
R-squared |
0.5615 |
4.6.2. Permutation Feature Importance
The importance of each feature is determined by its influence on the model’s predictions, known as feature importance. A higher value indicates a more significant impact on the model’s output. The feature “ArrivalPattern” has the highest importance score of 0.0659, “Weather” follows with a score of 0.039, “Time of day” has a score of 0.0331, “TravelBehaviour” with a score of 0.0322, and “MetroSatisfaction” has the lowest importance score of 0.0277. As presented in Table 10 and Figure 14 below.
Table 10. Permutation feature importance results.
Feature |
Importance |
Weather |
0.039 |
Time of day |
0.0331 |
Arrival Pattern |
0.0659 |
Travel Behaviour |
0.0322 |
Metro Satisfaction |
0.0277 |
Figure 14. Permutation feature importance.
The radar chart above visually represents the relative importance of five features (Weather, Time of day, arrival pattern, travel behavior, and MetroSatisfaction) in predicting the target variable. Each feature is plotted on an axis radiating from the center, with importance scores ranging from 0 to 0.07. The polygon formed by connecting the data points shows that ArrivalPattern has the highest importance, followed by Weather and Timeofday, while TravelBehaviour and MetroSatisfaction have lower importance scores. The chart’s shape and size provide a quick visual comparison, highlighting the most influential features clearly and intuitively.
4.6.3. Model Prediction
The Predicted and Residual Results table compares the predicted and actual values for passenger arrival, demonstrating that the neural network model’s predictions are generally close to the exact values. For instance, the expected value of 2.8202 is near the actual value of 2.6667, and the predicted value of 3.0717 is close to 3.0000. The minor discrepancies between the predicted and actual values, known as residuals, indicate slight overestimations by the model, such as a residual of 0.1535 for the first row and 0.0717 for the second row. These small residuals suggest that the model’s predictions are pretty accurate, but there is still room for improvement. The consistency of the residuals across different rows indicates that the model is systematically close to the actual values, but fine-tuning the model or incorporating additional features could further enhance its predictive accuracy. As presented in Table 11 and Figure 15 below.
Table 11. Predicted and residual results.
Predicted |
Actual |
2.8202 |
2.6667 |
3.0717 |
3.0000 |
2.2914 |
2.3333 |
2.6445 |
2.6667 |
2.5540 |
2.6667 |
2.6034 |
2.6667 |
Figure 15. Model prediction vs actual values.
The scatter plot includes more prominent, semi-transparent blue points to represent the model’s predictions, a red dashed line indicating the ideal scenario where predicted values match actual values, and a green solid line showing the linear regression fit. The plot also features a title, subtitle, and improved axis labels for better readability.
However, the feature Arrival Pattern shows positive and negative impacts on the model’s predictions, with contributions ranging from −0.0565 to 0.7223 and a variable value of 0.03585, indicating its variability in influencing passenger arrival. Metro Satisfaction consistently has a negative impact, with contributions ranging from −0.7273 to −0.7105 and a variable value of −0.9683, suggesting that lower satisfaction decreases passenger arrival predictions. Time of Day consistently has a positive impact, with contributions ranging from 0.0057 to 0.4969 and a variable value of 1.081, indicating that certain times of the day increase passenger arrival predictions. Travel Behaviour consistently has a negative impact, with contributions ranging from −0.2914 to −1.4838 and a variable value of 1.306, suggesting that certain travel behaviors decrease passenger arrival predictions. Lastly, Weather consistently has a positive impact, with contributions ranging from 0.1149 to 0.2093 and a variable value of 0.6323, indicating that certain weather conditions increase passenger arrival predictions. As presented in Table 12 and Figure 16 below.
![]()
Figure 16. Absolute SHAP values for the most influential features.
Table 12. Absolute SHAP values for each features.
Variable |
Contribution |
Variable value |
Sign |
Label |
B |
ArrivalPattern = 0.03585 |
−0.05651111 |
0.03585 |
−1 |
Neural Network |
0 |
MetroSatisfaction = −0.9683 |
−0.72726619 |
−0.9683 |
−1 |
Neural Network |
0 |
Timeofday = 1.081 |
0.00566966 |
1.081 |
1 |
Neural Network |
0 |
TravelBehaviour = 1.306 |
−0.29139648 |
1.306 |
−1 |
Neural Network |
0 |
weather = 0.6323 |
0.2093084 |
0.6323 |
1 |
Neural Network |
0 |
MetroSatisfaction = −0.9683 |
−0.71054409 |
−0.9683 |
−1 |
Neural Network |
0 |
The bar plot above, shows the variable values for each feature, with colors indicating the sign of the contribution (positive, negative, or both). The contribution range is annotated above each bar, providing additional context on each feature’s impact variability. This visualization helps summarize and compare each feature’s influence on the model’s predictions. Please let me know if you have any further questions or need additional analysis.
4.7. Model Comparison
4.7.1. Association Rule Mining
High passenger volumes are commonly associated with extended waiting times, weather-related disruptions, and peak-hour usage. On the other hand, moderate passenger volumes tend to have neutral stances on waiting times and weather impact. In contrast, low passenger volumes are strongly correlated with strong disagreement on waiting times, weather impact, and metro satisfaction. The use of color intensity and point size in visualizing association rules offers a clear and concise understanding of the strength of the relationships between these various factors and passenger traffic volumes.
4.7.2. Neural Network Model
A Mean Squared Error (MSE) measure of 0.149 indicates that the model’s predictions were fairly precise. The R-squared value of 0.866 suggests that the model can account for approximately 86.6% of the variation in passenger arrival volume. Among the features, waiting time had the highest importance score of 7.058, followed by metro satisfaction, with a score of 6.119 and weather with a score of 1.031. Although, time of day, arrival pattern, and travel behavior also played a significant role, their importance scores were lower. The model predictions were remarkably close to the actual values, with residuals ranging between -0.007 and 0.035 units for the Lagos Blue Light Rail Line.
However, for the Wuhan data set, the R-squared value of 0.561 suggests that the model can account for approximately 56.1% of the variation in passenger arrival volume. Among the features Arrival pattern had the highest importance score of 0.065, weather had 0.039 followed by time of day, with a score of 0.033. The expected value of 2.820 is near the actual value of 2.666, and the predicted value of 3.071 is close to 3.000, suggesting that the neural network model’s predictions are generally close to the exact values.
4.7.3. Concluding Remark
After a comprehensive analysis of the two models, it is clear that the neural network model surpasses in terms of predictive capabilities due to its superior accuracy and reduced error rates. Additionally, association rule mining provides additional insights by discovering intricate relationships that might not be apparent through conventional regression analysis. The neural network model’s superior predictive performance, as demonstrated by its increased accuracy and decreased error rates, makes it the preferred choice among the evaluated models. With its more dependable and precise approach to forecasting passenger arrival volumes, this model can be invaluable for transportation planning and operations.
5. Discussion
During the field observation at the Yujiatou, Jiangshe 2nd Road line 5, and Qingnian Road line 2 in Wuhan, there were distinct patterns in passenger arrival volumes across different times of the day and weather conditions. On weekdays, morning and evening peak hours consistently showed higher passenger volumes than off-peak hours, reflecting typical commuter patterns. Visual representations in Figure 1, Figure 2, and Figure 3 indicate the proportion of passenger volume at different times of the day, categorized by weekdays and weekends for each week. The data consistently showed that passenger volumes on weekdays were more significant than on weekends, reflecting typical commuter patterns. In July 2023, during the summer vacation, there was an increase in off-peak hour travel on weekends, indicating more leisure travel. In May 2024, not coinciding with the peak holiday season, there was a more balanced distribution of travel times, with a noticeable increase in evening peak hour travel compared to July 2023. This is in line with the findings of previous studies [15].
The association rule mining analysis revealed significant correlations between various factors and passenger arrival volumes. One rule showed a strong association between rainy weather in the evening and higher passenger arrival volumes, with a confidence level of 41.5%. Another rule indicated that extreme weather conditions, such as rain and heat, have a minor impact on passenger arrivals despite a low support value but significantly and negatively impact passenger arrivals when they occur. These findings are consistent with previous studies, as supported by the manual data Table 2. However, the neural network model, which offers accuracy with an MSE of 0.149 and an R-squared value of 0.86, suggests that weather is not the strongest predictor of passenger volume as shown in Table 6. The study further notes that the impact of weather on travel behavior varies depending on the mode of transportation used and is contingent on it. The seasonal variations observed in the data, with increased off-peak and weekend travel during the summer holiday, suggest that seasonal factors can significantly influence passenger arrival patterns and the overall usage of the metro system. These findings are consistent with the of previous studies [16] [18] [19].
Similarly, association rule mining reveals that passengers are 1.258 times more likely to travel during peak hours than off-peak hours Table 4. This is consistent with typical commuter patterns, where passenger volumes are higher during peak hours owing to work schedules and daily routines. The neural network model also supports this, with importance value of 0.89. The manual data, as shown in Table 2, also supports this finding, with higher passenger volumes recorded during the morning and evening peak hours compared to off-peak hours. Moreover, the arrival patterns and travel behaviour possess a lift value of 1.125 and 1.150 as shown in Figure 8. This study also demonstrated that passengers exhibit both random and non-random arrival patterns, which is supported by the literature [22]-[24]. The results however, indicate a strong correlation between shorter waiting times and higher passenger arrival volumes. The neural network model shows waiting time as the most significant feature, with an importance rating of 7.058 as shown in Figure 11. Additionally, association rule mining revealed a positive correlation, with a 33.6% probability of observing higher passenger arrival volumes when waiting times are shorter and a lift of 1.350, indicating that passenger arrival volumes are 1.350 times more likely to be higher when waiting times are shorter as presented in Table 4.
Moreover, the neural network model developed for the Wuhan dataset demonstrates promising performance, with a Root Mean Squared Error (RMSE) of 0.1970, indicating an average prediction error magnitude of 0.1970. The Mean Absolute Error (MAE) is 0.0921, suggesting that the model’s predictions are, on average, within 0.0921 of the actual values. The R-squared value of 0.5615 indicates that the model explains approximately 56.15% of the variance in the target variable, suggesting a moderate explanatory power Table 9. The permutation feature importance analysis reveals that the Arrival Pattern is the most influential feature, with an importance score of 0.0659, followed by Weather (0.039), Time of Day (0.0331), Travel Behaviour (0.0322), and Metro Satisfaction (0.0277) Table 10 and Figure 14. While the SHAP analysis shows that Arrival Pattern has positive and negative impacts on the model’s predictions, with contributions ranging from −0.0565 to 0.7223 and a variable value of 0.03585, indicating its variability in influencing passenger arrival. Metro Satisfaction consistently has a negative impact, with contributions ranging from −0.7273 to −0.7105 and a variable value of −0.9683, suggesting that lower satisfaction decreases passenger arrival predictions. Time of Day consistently has a positive impact, with contributions ranging from 0.0057 to 0.4969 and a variable value of 1.081, indicating that certain times of the day increase passenger arrival predictions. Travel Behaviour consistently has a negative impact, with contributions ranging from −0.2914 to −1.4838 and a variable value of 1.306, suggesting that certain travel behaviors decrease passenger arrival predictions. Lastly, Weather consistently has a positive impact, with contributions ranging from 0.1149 to 0.2093 and a variable value of 0.6323, indicating that certain weather conditions increase passenger arrival predictions Table 12 and Figure 16. Based on the findings of this study, the following recommendations are proposed for future research:
Replicating the study in other metropolitan areas or transportation systems could help validate the findings and explore the potential influence of regional or cultural differences on passenger arrival patterns.
Exploring advanced data collection techniques, such as sensor-based systems or intelligent card data, could provide more comprehensive and reliable data, enabling a deeper analysis of the research problem.
Investigating the impact of socioeconomic, demographic, and other contextual factors on passenger arrival patterns could yield additional insights and enhance the understanding of the underlying dynamics.
Conducting a longitudinal study over an extended period could provide valuable insights into the long-term trends and the influence of seasonal or other temporal factors on passenger arrival patterns.
Expanding the research to include the interactions between different modes of transportation, such as buses, trains, and private vehicles, could offer a more comprehensive understanding of passenger travel behavior and its implications for the overall transportation system.
By addressing these recommendations, future research can build upon the foundations laid by this study and contribute to the ongoing efforts to optimize metro systems and enhance the overall passenger experience.
6. Conclusions
In conclusion, the impact of weather, time of day, waiting time, metro satisfaction, arrival pattern, and travel behavior differs significantly between Wuhan Metro and Lagos Light Rail Blue Line, mainly due to efficiency, reliability, and advancement variations. The advanced nature of Wuhan Metro minimizes the influence of these factors, whereas, in the developing and underdeveloped Lagos Metro, they play a significantly impactful role. Within this context, this research has chosen to adopt the Neural Network Model for analysis in both cities. However, it’s crucial to acknowledge the study’s limitations that could affect its findings and future applications:
In the absence of Automatic Fare Collection Data, the reliance on direct field observation, passenger arrival volume statistics, revenue, and other online sources from Wuhan and Lagos may limit the depth and accuracy of the analysis.
Despite neural networks’ promising performance and other machine learning models’ accuracy and generalizability could be enhanced by incorporating more diverse factors.
The failure to consider the potential influence of land use, socio-economic, and demographic factors has possibly omitted critical context that could offer a more comprehensive understanding of the observed phenomena.
Acknowledging these limitations is essential for interpreting the results and guiding future research directions, especially in enhancing the Lagos Metro’s development and improvement, where the insights from this study are expected to be most beneficial.
Acknowledgements
I am profoundly thankful for the collective efforts of all those involved, making this research a truly rewarding and fulfilling endeavor. The successful completion of this study is a testament to the support, guidance, and contribution of many individuals. I am deeply grateful for their involvement with this study.
Funding
This research received no external funding.
Appendices
Appendix A
Table A1. Direct field observation of metro passenger arrival volume.
Date: 15th-21st May 2023 (Week 1)
Yujiatou Station
Weekdays |
Weekends |
Morning peak hour |
Weather |
23˚C/Fair |
24˚C/cloudy |
22˚C/Heavy rainfall |
22˚C/Fair |
24˚C/Fair |
24˚C/Fog |
24˚C/Fair |
Time interval |
Mon. |
Tue. |
Wed. |
Thurs. |
Fri. |
Sat. |
Sun. |
7:00am - 7:20am |
420 |
422 |
200 |
394 |
450 |
195 |
103 |
7:20am - 7:40am |
573 |
547 |
350 |
552 |
539 |
247 |
149 |
7:40 am - 8:00 am |
761 |
624 |
360 |
582 |
457 |
300 |
155 |
Total |
1754 |
1593 |
910 |
1528 |
1446 |
742 |
407 |
Off-peak hour |
|
30˚C/cloudy |
24˚C/cloudy |
24˚C/cloudy |
29˚C/Fair |
29˚C/Sunny |
27˚C/Sunny |
26˚C/Light rain |
12:00pm - 12:20pm |
129 |
77 |
80 |
120 |
110 |
240 |
125 |
12:20pm - 12:40pm |
87 |
147 |
110 |
93 |
123 |
158 |
90 |
12:40pm - 01:00pm |
104 |
128 |
106 |
128 |
108 |
203 |
192 |
Total |
320 |
352 |
296 |
341 |
341 |
601 |
407 |
Evening peak hour |
|
23˚C/cloudy |
28˚C/rainfal |
24˚C/Fair |
31˚C/Sunny |
30˚C/Sunny |
29˚C/Sunny |
24˚C/Cloudy |
05:00pm - 05:20pm |
287 |
284 |
275 |
311 |
421 |
330 |
203 |
05:20pm - 05:40pm |
229 |
286 |
272 |
319 |
433 |
297 |
208 |
05:40pm - 06:00pm |
336 |
167 |
220 |
267 |
403 |
324 |
309 |
Total |
852 |
737 |
767 |
897 |
1257 |
951 |
720 |
|
|
|
|
|
|
|
|
Date:10th - 16thJuly 2023 |
|
|
|
|
|
|
Week 2 |
Weekdays |
Weekends |
Morning peak hour |
Weather |
30˚C/Sunny |
33˚C/sunny |
33˚C/sunny |
33˚C/sunny |
28˚C/sunny |
30˚C/sunny |
28˚C/fair |
Time interval |
Mon. |
Tue. |
Wed. |
Thurs. |
Fri. |
Sat. |
Sun. |
8:00 am - 8:20am |
615 |
526 |
495 |
503 |
562 |
289 |
162 |
Off-peak hour |
|
30˚C/sunny |
36˚C/sunny |
36˚C/sunny |
36˚C/sunny |
28˚C/Sunny |
32˚C/fair |
31˚C/fair |
12:00pm - 12:20pm |
105 |
81 |
87 |
95 |
113 |
145 |
119 |
Evening peak hour |
|
30˚C/sunny |
36˚C/sunny |
37˚C/sunny |
35˚C/sunny |
26˚C/Fair |
32˚C/fair |
31˚C/fair |
06:00pm - 06:20pm |
287 |
272 |
247 |
244 |
307 |
225 |
160 |
Date: 17th - 24thJuly 2023 |
|
|
|
|
|
|
Week 3 |
Weekdays |
Weekends |
Morning peak hour |
Temperature |
27˚C/Partly cloud |
28˚C/Partly cloud |
28˚C/fair |
28˚C/light rain |
29˚C/Partly cloud |
28˚C/cloudy |
28˚C/sunny |
Time interval |
Mon. |
Tue. |
Wed. |
Thurs. |
Fri. |
Sat. |
Sun. |
8:00 am - 8:20am |
527 |
501 |
478 |
548 |
525 |
197 |
159 |
Off-peak hour |
Temperature |
30˚C/Partly cloud |
31˚C/Partly cloud |
30˚C/light rain |
30˚C/Partly cloud |
33˚C/sunny |
29˚C/Partly cloud |
33˚C/Partly cloud |
12:00pm - 12:20pm |
94 |
110 |
95 |
89 |
80 |
159 |
132 |
Evening peak hour |
Temperature |
30˚C/fair |
29˚C/Heavy rain |
27˚C/Heavy rain |
28˚C/Heavy rain |
29˚C/cloud |
32˚C/cloudy |
34˚C/Partly cloud |
06:00pm - 06:20pm |
245 |
234 |
223 |
195 |
307 |
246 |
230 |
Date: 20th -26th May 2024 (Week 4)
Weekdays |
Weekends |
Morning peak hour |
Weather |
24˚C/Fair |
24˚C/cloudy |
22˚C/Heavy rainfall |
24˚C/Fair |
26˚C/Fair |
24˚C/Fog |
27˚C/Fair |
Time interval |
Mon. |
Tue. |
Wed. |
Thurs. |
Fri. |
Sat. |
Sun. |
7:00am - 7:20am |
339 |
295 |
274 |
312 |
289 |
186 |
126 |
7:20am - 7:40am |
454 |
439 |
338 |
437 |
419 |
226 |
159 |
7:40 am - 8:00 am |
638 |
550 |
483 |
580 |
467 |
245 |
175 |
Total |
1431 |
1238 |
1095 |
1329 |
1446 |
657 |
460 |
Off-peak hour |
|
27˚C/fair |
24˚C/cloudy |
24˚C/cloudy |
27˚C/Fair |
27˚C/Sunny |
27˚C/Sunny |
33˚C/partly cloud |
12:00pm - 12:20pm |
148 |
201 |
130 |
79 |
116 |
185 |
162 |
12:20pm - 12:40pm |
132 |
133 |
127 |
90 |
128 |
167 |
160 |
12:40pm - 01:00pm |
127 |
133 |
138 |
100 |
113 |
193 |
185 |
Total |
407 |
467 |
395 |
269 |
357 |
545 |
507 |
Evening peak hour |
|
27˚C/cloudy |
28˚C/cloudy |
27˚C/Fair |
31˚C/Sunny |
32˚C/Sunny |
29˚C/Sunny |
33˚C/Cloudy |
05:00pm - 05:20pm |
291 |
297 |
245 |
326 |
287 |
265 |
230 |
05:20pm - 05:40pm |
241 |
296 |
237 |
466 |
277 |
289 |
208 |
05:40pm - 06:00pm |
283 |
326 |
321 |
437 |
354 |
287 |
215 |
Total |
815 |
919 |
803 |
1229 |
1257 |
841 |
653 |
Date: 27th May-02 June 2024 (Week 5)
Qingnian Road Station
Weekdays |
Weekends |
Morning peak hour |
Weather |
23˚C/cloudy |
21˚C/cloudy |
23˚C/sunny |
19˚C/light rain |
21˚C/Fog |
23˚C/clear |
24˚C/sunny |
Time interval |
Mon. |
Tue. |
Wed. |
Thurs. |
Fri. |
Sat. |
Sun. |
7:00am - 7:20am |
380 |
340 |
320 |
215 |
450 |
220 |
130 |
7:20am - 7:40am |
500 |
470 |
380 |
246 |
590 |
280 |
170 |
7:40 am - 8:00 am |
790 |
850 |
520 |
480 |
630 |
340 |
190 |
Total |
1670 |
1660 |
1230 |
941 |
1670 |
840 |
490 |
Off-peak hour |
|
28˚C/sunny |
28˚C/cloudy |
31˚C/sunny |
19˚C/light rain |
24˚C/fog |
29˚C/passing cloud |
28˚C/passing cloud |
12:00pm - 12:20pm |
150 |
140 |
160 |
110 |
130 |
120 |
110 |
12:20pm - 12:40pm |
170 |
160 |
180 |
130 |
150 |
140 |
102 |
12:40pm - 01:00pm |
190 |
170 |
200 |
150 |
160 |
170 |
136 |
Total |
510 |
470 |
540 |
390 |
440 |
430 |
348 |
Evening peak hour |
|
28˚C/cloudy |
30˚C/cloudy |
29˚C/cloudy |
21˚C/light rain |
26˚C/haze |
29˚C/Sunny |
26˚C/sunny |
05:00pm - 05:20pm |
491 |
400 |
396 |
302 |
561 |
330 |
322 |
05:20pm - 05:40pm |
361 |
403 |
500 |
433 |
600 |
389 |
415 |
05:40pm - 06:00pm |
726 |
668 |
898 |
445 |
880 |
415 |
700 |
Total |
1578 |
1468 |
1794 |
1180 |
2041 |
1134 |
1435 |
Date: 03rd - 09th June 2024 (Week 6)
Jiangshe 2nd Road
Weekdays |
Weekends |
Morning peak hour |
Weather |
21˚C/cloudy |
22˚C/cloudy |
19˚C/cloudy |
20˚C/fog |
24˚C/clear |
25˚C/sunny |
27˚C/Fair |
Time interval |
Mon. |
Tue. |
Wed. |
Thurs. |
Fri. |
Sat. |
Sun. |
7:00am - 7:20am |
339 |
350 |
304 |
294 |
350 |
200 |
206 |
7:20am - 7:40am |
354 |
300 |
328 |
352 |
339 |
199 |
220 |
7:40 am - 8:00 am |
438 |
380 |
303 |
382 |
357 |
215 |
187 |
Total |
1131 |
1030 |
935 |
1028 |
1046 |
614 |
613 |
Off-peak hour |
|
26˚C/cloud |
26˚C/haze |
20˚C/light rain |
25˚C/cloudy |
26˚C/cloudy |
31˚C/cloudy |
33˚C/cloudy |
12:00pm - 12:20pm |
200 |
190 |
180 |
199 |
185 |
215 |
200 |
12:20pm - 12:40pm |
210 |
200 |
190 |
210 |
200 |
205 |
198 |
12:40pm - 01:00pm |
205 |
195 |
185 |
206 |
220 |
204 |
220 |
Total |
615 |
585 |
555 |
615 |
605 |
624 |
618 |
Evening peak hour |
|
25˚C/cloudy |
22˚C/sunny |
21˚C/light rain |
27˚C/cloudy |
27˚C/sunny |
31˚C/sunny |
34˚C/sunny |
05:00pm - 05:20pm |
300 |
299 |
276 |
330 |
350 |
289 |
210 |
05:20pm - 05:40pm |
315 |
288 |
289 |
300 |
390 |
297 |
230 |
05:40pm - 06:00pm |
305 |
400 |
387 |
308 |
410 |
324 |
387 |
Total |
920 |
987 |
952 |
938 |
1150 |
910 |
827 |
Appendix B
Table B1. Lagos light rail blue line passenger arrival volume statistics and revenue.
Safety Production Day |
Date |
Days |
average daily passenger arrival volume |
daily train trips |
daily revenue (confidential) |
1 |
9/4/2023 |
Mon. |
1008 |
10 |
353,825 |
2 |
9/5/2023 |
Tues. |
1301 |
12 |
467,525 |
3 |
9/6/2023 |
Wed. |
1592 |
12 |
583,225 |
4 |
9/7/2023 |
Thur. |
1920 |
12 |
700,375 |
5 |
9/8/2023 |
Fri. |
2488 |
12 |
911,350 |
6 |
9/9/2023 |
Sat. |
1453 |
12 |
531,500 |
7 |
9/10/2023 |
Sun. |
343 |
12 |
123,600 |
8 |
9/11/2023 |
Mon. |
2924 |
12 |
1,074,975 |
9 |
9/12/2023 |
Tues. |
2511 |
12 |
917,900 |
10 |
9/13/2023 |
Wed. |
3145 |
12 |
1,156,225 |
11 |
9/14/2023 |
Thur. |
2844 |
12 |
1,048,050 |
12 |
9/15/2023 |
Fri. |
3337 |
12 |
1,201,125 |
13 |
9/16/2023 |
Sat. |
980 |
12 |
361,425 |
14 |
9/17/2023 |
Sun. |
257 |
12 |
89,700 |
15 |
9/18/2023 |
Mon. |
3742 |
12 |
1,383,850 |
16 |
9/19/2023 |
Tues. |
3626 |
12 |
1,335,900 |
17 |
9/20/2023 |
Wed. |
3390 |
12 |
1,242,650 |
18 |
9/21/2023 |
Thur. |
2737 |
12 |
997,575 |
19 |
9/22/2023 |
Fri. |
3151 |
12 |
1,161,950 |
20 |
9/23/2023 |
Sat. |
1805 |
12 |
657,925 |
21 |
9/24/2023 |
Sun. |
218 |
12 |
79,525 |
22 |
9/25/2023 |
Mon. |
4065 |
12 |
1,462,425 |
23 |
9/26/2023 |
Tues. |
4400 |
12 |
1,625,175 |
24 |
9/27/2023 |
Wed. |
2872 |
12 |
1,046,925 |
25 |
9/28/2023 |
Thur. |
3176 |
12 |
1,175,525 |
26 |
9/29/2023 |
Fri. |
3176 |
12 |
1,718,600 |
27 |
9/30/2023 |
Sat. |
2142 |
12 |
790,550 |
28 |
10/1/2023 |
Sun. |
245 |
12 |
87,975 |
29 |
10/2/2023 |
Mon. |
2549 |
12 |
956,350 |
30 |
10/3/2023 |
Tues. |
4601 |
12 |
1,684,350 |
31 |
10/4/2023 |
Wed. |
4014 |
12 |
1,474,775 |
32 |
10/5/2023 |
Thur. |
3199 |
12 |
1,184,100 |
33 |
10/6/2023 |
Fri. |
3808 |
12 |
1,416,775 |
34 |
10/7/2023 |
Sat. |
1823 |
12 |
675,150 |
35 |
10/8/2023 |
Sun. |
210 |
12 |
74,000 |
36 |
10/9/2023 |
Mon. |
4000 |
12 |
1,574,900 |
37 |
10/10/2023 |
Tues. |
4593 |
12 |
1,672,950 |
38 |
10/11/2023 |
Wed. |
4634 |
12 |
1,750,025 |
39 |
10/12/2023 |
Thur. |
3413 |
12 |
1,280,725 |
40 |
10/13/2023 |
Fri. |
4469 |
12 |
1,669,675 |
41 |
10/14/2023 |
Sat. |
549 |
5 |
205,275 |
42 |
10/15/2023 |
Sun. |
0 |
|
|
43 |
10/16/2023 |
Mon. |
7458 |
54 |
2,747,600 |
44 |
10/17/2023 |
Tues. |
7177 |
54 |
2,642,625 |
45 |
10/18/2023 |
Wed. |
8467 |
54 |
3,102,375 |
46 |
10/19/2023 |
Thur. |
7607 |
54 |
2,813,075 |
47 |
10/20/2023 |
Fri. |
8775 |
54 |
3,224,625 |
48 |
10/21/2023 |
Sat. |
5000 |
54 |
1,845,175 |
49 |
10/22/2023 |
Sun. |
284 |
22 |
99,025 |
50 |
10/23/2023 |
Mon. |
9843 |
54 |
3,612,800 |
51 |
10/24/2023 |
Tues. |
9472 |
54 |
3,502,225 |
52 |
10/25/2023 |
Wed. |
9055 |
54 |
3,319,675 |
53 |
10/26/2023 |
Thur. |
8490 |
54 |
3,102,475 |
54 |
10/27/2023 |
Fri. |
9275 |
54 |
3,428,100 |
55 |
10/28/2023 |
Sat. |
5264 |
54 |
1,941,750 |
56 |
10/29/2023 |
Sun. |
305 |
22 |
109,325 |
57 |
10/30/2023 |
Mon. |
9399 |
54 |
3,466,175 |
58 |
10/31/2023 |
Tues. |
9369 |
54 |
3,448,800 |
59 |
11/1/2023 |
Wed. |
9348 |
54 |
3,436,575 |
60 |
11/2/2023 |
Thur. |
8934 |
54 |
3,294,175 |
61 |
11/3/2023 |
Fri. |
10,901 |
54 |
3,997,575 |
62 |
11/4/2023 |
Sat. |
6440 |
54 |
2,355,925 |
63 |
11/5/2023 |
Sun. |
435 |
22 |
152,375 |
64 |
11/6/2023 |
Mon. |
7223 |
54 |
5,267,600 |
65 |
11/7/2023 |
Tues. |
5501 |
54 |
3,054,310 |
66 |
11/8/2023 |
Wed. |
5649 |
54 |
3,090,620 |
67 |
11/9/2023 |
Thur. |
5570 |
54 |
3,086,210 |
68 |
11/10/2023 |
Fri. |
5711 |
54 |
3,118,040 |
69 |
11/11/2023 |
Sat. |
3611 |
54 |
1,929,810 |
70 |
11/12/2023 |
Sun. |
295 |
22 |
157,675 |
71 |
11/13/2023 |
Mon. |
6430 |
54 |
3,549,745 |
72 |
11/14/2023 |
Tues. |
5711 |
54 |
3,177,480 |
73 |
11/15/2023 |
Wed. |
5345 |
54 |
2,958,755 |
74 |
11/16/2023 |
Thur. |
4925 |
54 |
2,725,410 |
75 |
11/17/2023 |
Fri. |
7131 |
54 |
3,978,220 |
76 |
11/18/2023 |
Sat. |
4852 |
54 |
2,695,950 |
77 |
11/19/2023 |
Sun. |
265 |
22 |
144,925 |
78 |
11/20/2023 |
Mon. |
8799 |
54 |
4,911,265 |
79 |
11/21/2023 |
Tues. |
10,029 |
54 |
5,588,870 |
80 |
11/22/2023 |
Wed. |
9066 |
54 |
5,016,905 |
81 |
11/23/2023 |
Thur. |
6262 |
54 |
3,479,570 |
82 |
11/24/2023 |
Fri. |
7631 |
54 |
4,232,750 |
83 |
11/25/2023 |
Sat. |
4140 |
54 |
2,300,740 |
84 |
11/26/2023 |
Sun. |
211 |
22 |
112,860 |
85 |
11/27/2023 |
Mon. |
7881 |
54 |
4,344,570 |
86 |
11/28/2023 |
Tues. |
7301 |
54 |
4,049,930 |
87 |
11/29/2023 |
Wed. |
6927 |
54 |
3,823,455 |
88 |
11/30/2023 |
Thur. |
6622 |
54 |
3,601,145 |
89 |
12/1/2023 |
Fri. |
6859 |
54 |
3,782,400 |
90 |
12/2/2023 |
Sat. |
4513 |
54 |
2,485,380 |
91 |
12/3/2023 |
Sun. |
286 |
22 |
154,825 |
92 |
12/4/2023 |
Mon. |
7599 |
54 |
4,225,365 |
93 |
12/5/2023 |
Tues. |
7144 |
54 |
3,984,410 |
94 |
12/6/2023 |
Wed. |
7339 |
54 |
4,098,160 |
95 |
12/7/2023 |
Thur. |
6740 |
54 |
3,743,230 |
96 |
12/8/2023 |
Fri. |
7631 |
54 |
4,257,030 |
97 |
12/9/2023 |
Sat. |
5252 |
54 |
2,920,020 |
98 |
12/10/2023 |
Sun. |
258 |
22 |
138,360 |
99 |
12/11/2023 |
Mon. |
7735 |
54 |
4,322,580 |
100 |
12/12/2023 |
Tues. |
8464 |
54 |
4,706,765 |
101 |
12/13/2023 |
Wed. |
9365 |
54 |
5,166,555 |
102 |
12/14/2023 |
Thur. |
7609 |
54 |
4,228,940 |
103 |
12/15/2023 |
Fri. |
8066 |
54 |
4,499,540 |
104 |
12/16/2023 |
Sat. |
5435 |
54 |
3,038,295 |
105 |
12/17/2023 |
Sun. |
316 |
22 |
170,280 |
106 |
12/18/2023 |
Mon. |
8147 |
54 |
4,554,440 |
107 |
12/19/2023 |
Tues. |
7809 |
54 |
4,374,705 |
108 |
12/20/2023 |
Wed. |
6900 |
54 |
3,825,805 |
109 |
12/21/2023 |
Thur. |
7259 |
54 |
4,035,275 |
110 |
12/22/2023 |
Fri. |
7517 |
54 |
4,199,005 |
111 |
12/23/2023 |
Sat. |
5405 |
54 |
3,010,850 |
112 |
12/24/2023 |
Sun. |
393 |
22 |
213,165 |
113 |
12/25/2023 |
Mon. |
1294 |
54 |
698,810 |
114 |
12/26/2023 |
Tues. |
3131 |
54 |
1,735,280 |
115 |
12/27/2023 |
Wed. |
5557 |
54 |
3,093,460 |
116 |
12/28/2023 |
Thur. |
5377 |
54 |
2,981,455 |
117 |
12/29/2023 |
Fri. |
4941 |
54 |
2,752,680 |
118 |
12/30/2023 |
Sat. |
4087 |
54 |
2,270,525 |
119 |
12/31/2023 |
Sun. |
386 |
22 |
211,545 |
120 |
1/1/2024 |
Mon. |
1329 |
54 |
710,120 |
121 |
1/2/2024 |
Tues. |
3208 |
54 |
1,773,730 |
122 |
1/3/2024 |
Wed. |
3394 |
54 |
1,881,360 |
123 |
1/4/2024 |
Thur. |
3317 |
54 |
1,827,120 |
124 |
1/5/2024 |
Fri. |
3546 |
54 |
1,958,955 |
125 |
1/6/2024 |
Sat. |
2418 |
54 |
1,339,490 |
126 |
1/7/2024 |
Sun. |
344 |
22 |
184,140 |
127 |
1/8/2024 |
Mon. |
4691 |
54 |
2,612,635 |
128 |
1/9/2024 |
Tues. |
4333 |
54 |
2,412,815 |
129 |
1/10/2024 |
Wed. |
4569 |
54 |
2,549,080 |
130 |
1/11/2024 |
Thur. |
4403 |
54 |
2,450,030 |
131 |
1/12/2024 |
Fri. |
4569 |
54 |
2,493,870 |
132 |
1/13/2024 |
Sat. |
2840 |
54 |
1,580,835 |
133 |
1/14/2024 |
Sun. |
253 |
22 |
132,755 |
134 |
1/15/2024 |
Mon. |
6306 |
54 |
3,509,490 |
135 |
1/16/2024 |
Tues. |
5961 |
54 |
3,317,670 |
136 |
1/17/2024 |
Wed. |
5888 |
54 |
3,271,320 |
137 |
1/18/2024 |
Thur. |
5216 |
54 |
2,855,745 |
138 |
1/19/2024 |
Fri. |
6035 |
54 |
3,349,895 |
139 |
1/20/2024 |
Sat. |
3167 |
54 |
1,751,205 |
140 |
1/21/2024 |
Sun. |
229 |
22 |
121,055 |
141 |
1/22/2024 |
Mon. |
6881 |
54 |
3,812,185 |
142 |
1/23/2024 |
Tues. |
6120 |
54 |
3,400,770 |
143 |
1/24/2024 |
Wed. |
6173 |
54 |
3,417,775 |
144 |
1/25/2024 |
Thur. |
5211 |
54 |
2,881,130 |
145 |
1/26/2024 |
Fri. |
5390 |
54 |
2,981,680 |
146 |
1/27/2024 |
Sat. |
3269 |
54 |
1,795,685 |
147 |
1/28/2024 |
Sun. |
213 |
22 |
115,580 |
148 |
1/29/2024 |
Mon. |
6262 |
54 |
4,216,515 |
149 |
1/30/2024 |
Tues. |
4937 |
54 |
3,606,865 |
150 |
1/31/2024 |
Wed. |
4454 |
54 |
3,275,840 |
151 |
2/1/2024 |
Thur. |
3922 |
54 |
2,860,280 |
152 |
2/2/2024 |
Fri. |
4386 |
54 |
3,206,580 |
153 |
2/3/2024 |
Sat. |
2106 |
54 |
1,545,725 |
154 |
2/4/2024 |
Sun. |
169 |
22 |
127,785 |
155 |
2/5/2024 |
Mon. |
4775 |
54 |
3,510,960 |
156 |
2/6/2024 |
Tues. |
4465 |
54 |
3,242,685 |
157 |
2/7/2024 |
Wed. |
4134 |
54 |
3,157,450 |
158 |
2/8/2024 |
Thur. |
3926 |
54 |
2,861,575 |
159 |
2/9/2024 |
Fri. |
4130 |
54 |
3,043,080 |
160 |
2/10/2024 |
Sat. |
1921 |
54 |
1,412,415 |
161 |
2/11/2024 |
Sun. |
159 |
22 |
114,160 |
162 |
2/12/2024 |
Mon. |
4777 |
54 |
3,509,055 |
163 |
2/13/2024 |
Tues. |
4461 |
54 |
3,255,380 |
164 |
2/14/2024 |
Wed. |
4319 |
54 |
3,141,260 |
165 |
2/15/2024 |
Thur. |
3767 |
54 |
2,762,510 |
166 |
2/16/2024 |
Fri. |
3975 |
54 |
2,964,300 |
167 |
2/17/2024 |
Sat. |
2116 |
54 |
1,565,750 |
168 |
2/18/2024 |
Sun. |
177 |
22 |
125,550 |
169 |
2/19/2024 |
Mon. |
4303 |
54 |
3,157,550 |
170 |
2/20/2024 |
Tues. |
3048 |
54 |
2,192,540 |
171 |
2/21/2024 |
Wed. |
5117 |
54 |
3,795,250 |
172 |
2/22/2024 |
Thur. |
3949 |
54 |
2,841,350 |
173 |
2/23/2024 |
Fri. |
5228 |
54 |
3,828,100 |
174 |
2/24/2024 |
Sat. |
948 |
31 |
698,660 |
175 |
2/25/2024 |
Sun. |
189 |
22 |
136,210 |
176 |
2/26/2024 |
Mon. |
5838 |
54 |
3,258,430 |
177 |
2/27/2024 |
Tues. |
5629 |
54 |
3,108,600 |
178 |
2/28/2024 |
Wed. |
8114 |
54 |
4,452,305 |
179 |
2/29/2024 |
Thur. |
6489 |
54 |
3,360,915 |
180 |
3/1/2024 |
Fri. |
6378 |
54 |
3,512,825 |
181 |
3/2/2024 |
Sat. |
2993 |
54 |
1,648,445 |
182 |
3/3/2024 |
Sun. |
221 |
22 |
119,580 |
183 |
3/4/2024 |
Mon. |
7536 |
54 |
4,110,765 |
184 |
3/5/2024 |
Tues. |
6464 |
54 |
3,543,045 |
185 |
3/6/2024 |
Wed. |
7296 |
54 |
3,837,440 |
186 |
3/7/2024 |
Thur. |
7936 |
50 |
4,344,100 |
187 |
3/8/2024 |
Fri. |
7589 |
54 |
4,191,565 |
188 |
3/9/2024 |
Sat. |
3063 |
54 |
1,713,815 |
189 |
3/10/2024 |
Sun. |
269 |
22 |
144,960 |
190 |
3/11/2024 |
Mon. |
7325 |
54 |
4,056,305 |
191 |
3/12/2024 |
Tues. |
6923 |
54 |
3,844,725 |
192 |
3/13/2024 |
Wed. |
7628 |
54 |
4,236,665 |
193 |
3/14/2024 |
Thur. |
6892 |
54 |
3,806,375 |
194 |
3/15/2024 |
Fri. |
7011 |
54 |
3,880,100 |
195 |
3/16/2024 |
Sat. |
3133 |
54 |
1,735,130 |
196 |
3/17/2024 |
Sun. |
241 |
22 |
126,180 |
197 |
3/18/2024 |
Mon. |
7920 |
54 |
4,384,950 |
198 |
3/19/2024 |
Tues. |
7706 |
54 |
4,214,705 |
199 |
3/20/2024 |
Wed. |
8178 |
54 |
4,499,450 |
200 |
3/21/2024 |
Thur. |
7064 |
54 |
3,884,635 |
201 |
3/22/2024 |
Fri. |
8603 |
54 |
4,763,905 |
202 |
3/23/2024 |
Sat. |
3585 |
54 |
2,008,820 |
203 |
3/24/2024 |
Sun. |
214 |
22 |
116,185 |
204 |
3/25/2024 |
Mon. |
8113 |
54 |
4,517,645 |
205 |
3/26/2024 |
Tues. |
7910 |
54 |
4,058,220 |
206 |
3/27/2024 |
Wed. |
7185 |
54 |
3,982,150 |
207 |
3/28/2024 |
Thur. |
7716 |
54 |
4,295,145 |
208 |
3/29/2024 |
Fri. |
4303 |
54 |
2,403,850 |
209 |
3/30/2024 |
Sat. |
4276 |
54 |
2,221,410 |
210 |
3/31/2024 |
Sun. |
276 |
22 |
149,465 |
211 |
4/1/2024 |
Mon. |
2606 |
54 |
1,441,730 |
212 |
4/2/2024 |
Tues. |
6767 |
54 |
3,740,185 |
213 |
4/3/2024 |
Wed. |
6187 |
54 |
3,433,135 |
214 |
4/4/2024 |
Thur. |
5403 |
54 |
3,004,715 |
215 |
4/5/2024 |
Fri. |
5765 |
54 |
3,206,170 |
216 |
4/6/2024 |
Sat. |
2828 |
54 |
1,576,940 |
217 |
4/7/2024 |
Sun. |
192 |
22 |
103,350 |
218 |
4/8/2024 |
Mon. |
6287 |
54 |
3,485,580 |
219 |
4/9/2024 |
Tues. |
3220 |
54 |
1,801,395 |
220 |
4/10/2024 |
Wed. |
2523 |
54 |
1,389,880 |
221 |
4/11/2024 |
Thur. |
2632 |
54 |
1,449,840 |
222 |
4/12/2024 |
Fri. |
5474 |
54 |
3,025,605 |
223 |
4/13/2024 |
Sat. |
2751 |
54 |
1,538,200 |
224 |
4/14/2024 |
Sun. |
178 |
22 |
98,450 |
225 |
4/15/2024 |
Mon. |
6819 |
54 |
3,776,655 |
226 |
4/16/2024 |
Tues. |
6219 |
54 |
3,440,670 |
227 |
4/17/2024 |
Wed. |
6040 |
54 |
3,329,650 |
228 |
4/18/2024 |
Thur. |
5426 |
54 |
3,014,570 |
229 |
4/19/2024 |
Fri. |
5860 |
54 |
3,242,060 |
230 |
4/20/2024 |
Sat. |
3045 |
54 |
1,704,635 |
231 |
4/21/2024 |
Sun. |
251 |
22 |
135,120 |
232 |
4/22/2024 |
Mon. |
6558 |
54 |
3,632,680 |
233 |
4/23/2024 |
Tues. |
5680 |
54 |
3,139,400 |
234 |
4/24/2024 |
Wed. |
6330 |
54 |
3,495,865 |
235 |
4/25/2024 |
Thur. |
5636 |
54 |
3,075,670 |
236 |
4/26/2024 |
Fri. |
6158 |
54 |
3,354,505 |
237 |
4/27/2024 |
Sat. |
3388 |
54 |
1,882,535 |
238 |
4/28/2024 |
Sun. |
244 |
22 |
133,430 |
239 |
4/29/2024 |
Mon. |
7417 |
54 |
4,080,905 |
240 |
4/30/2024 |
Tues. |
7726 |
54 |
4,277,825 |
241 |
5/1/2024 |
Wed. |
3806 |
54 |
2,115,215 |
242 |
5/2/2024 |
Thur. |
6459 |
54 |
3,494,065 |
243 |
5/3/2024 |
Fri. |
6007 |
54 |
3,246,635 |
244 |
5/4/2024 |
Sat. |
3551 |
54 |
1,981,455 |
245 |
5/5/2024 |
Sun. |
224 |
22 |
121,565 |
246 |
5/6/2024 |
Mon. |
7498 |
54 |
4,142,005 |
247 |
5/7/2024 |
Tues. |
6932 |
54 |
3,836,915 |
248 |
5/8/2024 |
Wed. |
7508 |
54 |
4,165,400 |
249 |
5/9/2024 |
Thur. |
6598 |
54 |
3,651,870 |
250 |
5/10/2024 |
Fri. |
6764 |
54 |
3,743,140 |
251 |
5/11/2024 |
Sat. |
3160 |
54 |
1,761,710 |
252 |
5/12/2024 |
Sun. |
245 |
22 |
132,830 |
253 |
5/13/2024 |
Mon. |
6971 |
54 |
3,846,025 |
254 |
5/14/2024 |
Tues. |
7064 |
54 |
3,593,820 |
255 |
5/15/2024 |
Wed. |
6751 |
54 |
3,574,985 |
256 |
5/16/2024 |
Thur. |
5873 |
54 |
3,117,090 |
257 |
5/17/2024 |
Fri. |
6901 |
54 |
3,604,185 |
258 |
5/18/2024 |
Sat. |
3227 |
54 |
1,716,580 |
259 |
5/19/2024 |
Sun. |
224 |
22 |
117,665 |
260 |
5/20/2024 |
Mon. |
7313 |
54 |
3,834,029 |
261 |
5/21/2024 |
Tues. |
6537 |
54 |
3,594,330 |
262 |
5/22/2024 |
Wed. |
6183 |
54 |
3,394,930 |
263 |
5/23/2024 |
Thur. |
6396 |
54 |
3,523,461 |
264 |
5/24/2024 |
Fri. |
7796 |
54 |
4,313,655 |
265 |
5/25/2024 |
Sat. |
3553 |
54 |
1,978,420 |
266 |
5/26/2024 |
Sun. |
311 |
22 |
167,175 |
267 |
5/27/2024 |
Mon. |
8992 |
54 |
4,929,160 |
268 |
5/28/2024 |
Tues. |
7682 |
54 |
4,254,235 |
269 |
5/29/2024 |
Wed. |
5920 |
54 |
3,096,745 |
270 |
5/30/2024 |
Thur. |
5784 |
54 |
3,130,825 |
271 |
5/31/2024 |
Fri. |
6580 |
54 |
3,611,070 |
272 |
6/1/2024 |
Sat. |
3912 |
54 |
2,176,470 |
273 |
6/2/2024 |
Sun. |
301 |
22 |
165,290 |
274 |
6/3/2024 |
Mon. |
6823 |
54 |
4,938,305 |
275 |
6/4/2024 |
Tues. |
6218 |
54 |
4,571,485 |
276 |
6/5/2024 |
Wed. |
6461 |
54 |
4,748,825 |
277 |
6/6/2024 |
Thur. |
7117 |
54 |
5,252,405 |
278 |
6/7/2024 |
Fri. |
6563 |
54 |
4,841,745 |
279 |
6/8/2024 |
Sat. |
3145 |
54 |
2,321,345 |
280 |
6/9/2024 |
Sun. |
228 |
22 |
162,915 |
281 |
6/10/2024 |
Mon. |
6921 |
54 |
5,101,270 |
282 |
6/11/2024 |
Tues. |
6933 |
54 |
5,085,865 |
283 |
6/12/2024 |
Wed. |
3684 |
54 |
2,709,445 |
Appendix C
Table C1. Questionnaire content.
No |
Questions |
Abbreviation of the question name |
1 |
What is your age? |
|
2 |
What is your gender? |
|
3 |
What is your education level? |
|
4 |
Considering your recent experiences how would you rate the overall volume of passengers arriving at metro stations? |
PAR1 |
5 |
During the busiest times of the day, how would you rate the level of crowding on the metro? |
PAR2 |
6 |
Comparing to a year ago, how would you describe the change in metro passenger arrival volume? |
PAR3 |
7 |
Weather conditions (e.g. rain, snow, heat) impact metro passenger arrival volumes |
W1 |
8 |
The metro passenger arrival volume significantly increases during peak summer/winter. |
W2 |
9 |
What type of weather-related disruptions are most likely to stop you from coming to the metro station? |
W3 |
10 |
How likely are you to use alternative transportation or adjust your travel schedule due to congestion or delays during peak hours? |
TOD1 |
11 |
How would you rate the difference in metro passenger arrivals between peak and off-peak hours? |
TOD2 |
12 |
I perceive a significant increase in metro usage during evening peak hours. |
TOD3 |
13 |
Longer waiting times at metro stations result in more people using the metro. |
WAT1 |
14 |
Shorter waiting times lead to higher passenger arrival volumes at metro stations. |
WAT2 |
15 |
Perceived waiting time influences the number of passengers arriving at metro stations. |
WAT3 |
16 |
Satisfactory metro service leads to higher passenger arrival volumes. |
MSS1 |
17 |
I am more likely to use the metro when satisfied with its service. |
MSS2 |
18 |
Overall satisfaction with the metro positively influences the number of passengers using it. |
MSS3 |
19 |
How often do you change your travel plans in response to real-time information about metro passenger volume or congestion, which may impact passenger arrival volumes? |
TB1 |
20 |
To what extent do you agree that your travel behaviour, such as taking alternative modes of transportation or adjusting your travel time influences the rate of passenger arrivals at metro station? |
TB2 |
21 |
How frequently do you use the metro for commuting to work, social events, or school/university? |
TB3 |
22 |
To what extent do you agree that the pattern of passenger arrivals influence the overall passenger volume at the metro station? |
AP1 |
23 |
How important is understanding the passenger arrival pattern to you when planning your trip to the metro station? |
AP2 |
24 |
Do you observe a consistent pattern in the arrival of passengers at metro stations throughout the day?” |
AP3 |
PAR = Passenger Arrival Volume, TOD = Time of Day, W = Weather, WAT = Waiting Time, MSS = Metro Satisfaction, TB = Travel Behaviour, AP = Arrival Pattern.