Pedestrian Facilities Capacity and Level of Service at Intersections in a Connected and Autonomous Vehicle Environment

It is inevitable that Connected and Autonomous Vehicles (CAVs) will be a major focus of transportation and the automotive industry with increased use in future traffic system analysis. Numerous studies have focused on the evaluation and potential development of CAVs technology; however, pedestrians and bicyclists, as two essential and important modes of the road users have seen little to no coverage. In response to the need for analyzing the impact of CAVs on non-motorized transportation, this paper develops a new model for the evaluation of the Level of Service (LOS) for pedestrians in a CAVs environment based on the Highway Capacity Manual (HCM). The HCM provides a methodology to assess the level of service for pedestrians and bicyclists on various types of intersections in urban areas. Five scenarios were created for simulation via VISSIM (a software) that corresponds to the different proportions of the CAVs and different signal systems in a typical traffic environment. Alternatively, the Surrogate Safety Assessment Model (SSAM) was selected for analyzing the safety performance of the five scenarios. Through computing and analyzing the results of simulation and SSAM, the latter portion of this paper focuses on the development of a new model for evaluating pedestrian LOS in urban areas which are based upon HCM standards which are suitable for CAVs environments. The results of this study are intended to inform the future efforts of engineers and/or policymakers and to provide them with a tool to conduct a comparison of capacity and LOS related to the impact of CAVs on pedestrians during the process of a transportation system transition to CAVs.


Introduction
Given the advancement of sensor technologies, information technologies, and vehicle control technologies; the automation of control of vehicles has attracted much attention. The connected and autonomous technologies have become an essential part of tackling critical traffic safety, efficiency and convenience issues.
Major car companies, internet companies, universities and research institutes have successively launched prototype or concept models of CAVs. Governments are also promoting autonomous technologies. The emergence of CAVs also promoted discussion of coexistence issues with other road users: conventional vehicles and non-motorized traffic [1] [2] [3] [4] [5]. It has been determined that barriers exist in applying the intelligent driving technologies to the development and implementation of CAVs, especially for mixed traffic environments where pedestrians, bicyclists, manual driving cars are present. Although the use of CAV has tremendous potential to improve vehicle safety, congestion, travel costs, and freight movement; there are tradeoffs to be considered in safety cost, liability, insurance and policy. A literature search conducted recently shows 432 United States and international articles related to autonomous vehicle issues which identified fewer than 20 that discussed pedestrian or bicycle topics, either briefly or in depth [2]. The impact of CAVs on non-motorized transportation, especially for bicycles and pedestrians, is an important area of future study and analysis. The work of this paper will focus on pedestrians. Further; as we consider the forward design and evolution of mixed traffic environments, it is essential to consider, in a seamless way, the needs of all of road users [6] [7] [8].
Current research tends to be limited to the study of the impact of CAVs on facilities and safety aspects in a very general way. Additional work is needed to analyze these effects to better determine the influence of CAVs on pedestrians.
Further, in the life cycle of technology development there is an adaptation phase between market entry and potential societal integration which should not be minimized. This is especially true of CAV technology development which has the potential to completely change the way people travel. The intended contribution of this paper is to develop a new model to evaluate the LOS related to the impact of CAVs on pedestrians within the context of the process of adaptation.
Four distinct parts are included in this study. The first part contains a discussion of HCM which describes the LOS evaluation methods of pedestrians on signalized intersections in urban areas. The second part investigates the five scenarios development in VISSIM (a microscope multi-modal traffic flow simulation software) that corresponds to different ratios of CAVs in the transportation system (0%, 25%, 50%, 75%, and 100%). The third part introduces the SSAM software and uses this software to perform safety analysis on the simulation results from VISSIM. The fourth part establishes a model to evaluate the pedestrian LOS (PLOS), which is suitable for the road network in a CAVs environment.  [14]. However, from the federal level, related laws have been proposed in July 2015 [15]. This legislation allows inter-departmental investigation of the automotive network security problem. This is also the impetus used by the National Highway Traffic System Administration (NHTSA) for formulating and publishing the relevant regulations.  [9], b [10], c [11]. Journal of Transportation Technologies At this point, automatic driving technologies do not have the benefit of a legal construct that fully articulates the responsibility for motor vehicle traffic accidents. This legal quagmire is perhaps the largest obstacle impeding the further adoption of these technologies. It is imperative that new methods to clarify the legal liability of automatic driving by legislation be determined as quickly as possible to ease the adoption of these new and innovative technologies. Of note, is a trend where, as the role of the drivers shift in the automated driving system, the legal position for liability of traffic accidents is transferred from drivers to manufacturers, software designers, and other actors involved in design and production. Therefore, all countries require the automatic driving system to install a "black box" to record data, to identify the cause of the accidents and clarify the responsibilities of all parties [16].
At present, most countries stipulate a compulsory insurance system for road tests for self-driving cars. The commercialization of CAVs must have risk-controlled technology, which includes the participation of insurance companies. From the perspective of risk control expertise, insurance companies are willing to underwrite and make CAVs officially commercialized. The insurance company gives clear parameters that indicate that the risk of CAVs commercialization is under control. As long as there is a risk of safety, it still needs to be supported by the insurance industry.

Methods of PLOS Evaluation
Level of Service (LOS) is a quantitative assessment approach for measuring the performance of traffic modes and facilities [17] [18] [19] [20]. For the evaluation of PLOS, the earlier approaches depend on the LOS of vehicles [21]. The later research paid more attention to pedestrian safety, comfort, and flexibility. Finally, the methods in Highway Capacity Manual [22] proposed a complete system and the model is calibrated through numerous field experiments. The evaluation models in this system are not uniform: they are divided into various types for scenarios: segments; intersections; signalized and non-signalized intersections.

Methods of CAVs Intersection Coordination
The movement of CAVs at intersections can be described by two situations: the presence or absence of a signal system. The signal controllers for conventional vehicles aim to prioritize traffic movements and coordinate with neighboring controllers to avoid conflicting flows. Signal timing of intersections can be optimized to reduce the congestion delay. However, for CAVs, there exist more opportunities to minimize control delay through communication systems. By using advanced technologies of the Internet of Vehicles, traffic systems can avoid unnecessary vehicle stops and provide safe vehicle trajectories.
Some papers based on the dynamic adaptive control system, proposed the intelligent traffic management technology and mechanism for real-time traffic flow [23] [24]. Zhang et al. proposed a scheduling method based on vehicle status priority in traffic scenarios without traffic controls [25]. This method can ef-Journal of Transportation Technologies fectively prevent the collision of vehicles in the intersection by assigning a successive level of specific vehicles and avoiding the contradiction between the different vehicle travel trajectories. Some studies established the principle of a buffer allocation scheduling algorithm to provide a guide to vehicles to safely pass the intersections [26]. Figure 1 shows the intersection channelization.

Development of VISSIM Simulation Models
Traffic simulation refers to the use of computer simulation techniques to study traffic behavior. At this point, simulation is the best alternative since CAVs have not been widely used in the real traffic system. This paper will investigate the VISSIM simulation platform. VISSIM is a microscopic traffic flow simulation system developed by PTV of Germany. The system is a discrete, random, micro-

Scenarios Building
In the VISSIM simulation model, all the lane widths are set at 3.5 meters. For pedestrian crosswalks, the width is 3 meters, and for walkways, the width is set as 2 meters. Figure 2 shows the base network for all scenarios.
The vehicle traffic composition includes three types: human-driven vehicles, CAVs and freight vehicles (HGV). In this paper, two driving behaviors were selected: Urban (motorized) and AV (autonomous vehicles)-all-knowing. The AV all-knowing driving behavior model was developed by VISSIM and CoExist (a European project, aims at preparing the transition phase during which automated and conventional vehicles will co-exist on city roads). For Urban (motorized) behaviors, the number of interactive objects is set as 4. The standstill distance is 0.5 meters; and the car following model type is Wiedemann 74 (a car following model developed by Wiedemann in 1974). The basic concept of this model is that a driver with a high-speed vehicle will decelerate when reaching the driver self-feeling limit before reaching a low-speed vehicle. Since the driver cannot judge the speed of the low-speed car accurately, driver's speed will drop slower than that low-speed car until the driver starts to accelerate slightly after reaching another self-feeling limit. The result of this logic is an iterative process of acceleration and deceleration. For human-driven vehicles and HGV vehicle type, they follow the Urban (motorized) driving behavior; for CAVs, they follow the AV-all-knowing driving behavior. The traffic volume of each direction is a variable for developing the new models. But the split proportions are fixed.
About the pedestrian flows, same as vehicles, the volume of pedestrians in each base are variables and the route decisions are fixed which are same size ratio. The signal control system is a four-phase fixed system. The cycle time is 120 seconds. Every phase has 24 seconds green time, 5 seconds yellow time and 1 second all-red time. That means the inter-stage time is 5 seconds. One stage for each approach is 4 seconds. For the right turn movement from east to north, the green time is 52 seconds. Table 3 shows the basic setting of different scenarios.

CAVs Modeling in VISSIM
To define CAVs behavior in VISSIM, the logic is the same as normal vehicles.
There are five aspects included in driving control logic: conflict resolution, signal control, following behavior, lane change behavior and lateral behavior [27]. There are three predefined driving behaviors for different types of auto- To allow CAVs keep a small headway even during an acceleration process, a new parameter is used. This value usually defines a percentage that is larger than 100% of the average acceleration and using this value when the leading vehicle is accelerating.

Calibration and Validation of the VISSIM Model
The VISSIM model cannot provide the realistic results until the model is calibrated and validated [28] [29] [30]. Multiple calibration parameters offered by VISSIM can be modified. In this paper, the following calibration parameters were selected:  Average standstill distance;  Additive part of desired safety distance;  Multiple parts of desired safety distance;  Minimum headway;  Minimum gap time.
The number of conflicts was used to calibrate these parameters. Finally, it was found that changing the calibration parameters did not impact the number of conflicts. Therefore, in this case, the default value of parameters was used. Specifically, average standstill distance was 2 meters, additive part of desired safety distance was 3 meters, multiple parts of desired safety distance was 3 meters, the minimum headway gap was 5 meters, and the minimum gap time was 3 seconds. The calibrated models were validated with a new set of field data, including the pedestrian volumes, and the vehicle volumes. Finally, the animation of the VISSIM simulation models was checked for any unusual events. After these operation, VISSIM model was calibrated and validate.

Surrogate Safety Assessment Model (SSAM) Analysis
SSAM software can automate conflict analysis by directly processing vehicle trajectory data from VISSIM. It can provide a summary of the total number of conflicts broken down by type of conflict. Also, SSAM could calculate some surrogate safety measures for each event. SSAM was not explicitly designed for pedestrian conflict analysis, so there is no vehicle or entity type available in the trajectory file format by which to identify pedestrian conflicts. In other words, SSAM cannot estimate the pedestrian-to-vehicle conflicts without simulating the pedestrian as vehicles in VISSIM. Therefore, to identify pedestrian-to-vehicle conflicts from all kinds of conflicts, the CSV file exported by SSAM can be of help. Filter out any conflict where MaxS is smaller than 5 mph (7.3 ft/sec), and this conflict can be related to pedestrians (which is about the walking pace of pedestrians).
At the time this research was conducted the current version of SSAM only permitted the vehicle to vehicle conflict, yet VISSIM allowed the vehicle to pedestrian interactions. An alternative approach to the one described above was to use VISSIM for simulating the vehicle-pedestrian activities, store the trajectory files, then produce a video of the simulation activities. Playing the video back and manually observing the TTC and PET using the internal clock of the video would produce the needed data.
Two values for surrogate measures of safety were used in SSAM to detect the conflicts, which are maximum TTC and maximum PET. TTC is defined as the time distance to a collision of two road users if they keep their directions and velocities. PET is defined as the period from the moment when the first road user is leaving the conflict area until the second road user reaches it. When the maximum value of TTC is set as 1.5, then SSAM will only generate the conflict data that contains TTC value less than 1.5. In general, SSAM utilizes a default maximum TTC value of 1.5 seconds and a maximum PET value of 5 seconds to delineate the vehicle-vehicle conflicts. However, the pedestrian-vehicle conflict is different from vehicle-vehicle conflicts. That is why the maximum TTC and PET thresholds need to be established for pedestrian-vehicle conflicts.
Several trials were investigated to get the optimum thresholds for TTC and PET that would define a vehicle-pedestrian conflict. Finally, it was found that when the TTC threshold ranged from 2 to 3 and the PET ranged from 5 to 9, SSAM provided a better estimate of the number of conflicts that matched the field data. Table 4 shows the results of scenario 1 from SSAM software: All these three types include vehicle to vehicle collisions and pedestrian to vehicles collisions. From the previous discussion, when MaxS was smaller than 5 mph (7.3 ft/sec), the conflicts would count as pedestrian and vehicles conflicts.   For every scenario, multiple simulations were carried out with different values of the seven parameters. The average delay time and LOS values were extracted from VISSIM. The number of conflicts between pedestrians and vehicles were from SSAM. Each simulation corresponds to different set of these seven parameters plus the value of LOS. Results for the simulation are shown in Table 5.

Regression Analysis
This study uses JMP software for regression analysis. And the Equation (2) below shows the relationship between PLOS and seven parameters.
The coefficient of regression (R 2 ) is a vital statistic reflecting the goodness of fit of the model, which is the ratio of the sum of the squares of the regression to the sum of the total squares. R 2 takes values between 0 and 1 and has no units. The larger R 2 (close to 1), the better the fitted regression equation. In this model, the value of R 2 equal to 0.994 which means a strong fit.
For the contribution of each parameters, Figure 3 shows the Effect Summary of the new model. The Effect Summary option shows an interactive report. It gives a plot of the LogWorth (or FDR LogWorth) values for the effects in the model. The report also provides controls that enables one to add or remove effects from the model. From the model, the most contribution parameter is volume of human-driven vehicles and followed by the existence of signal system, delay time and volume of pedestrians. Journal of Transportation Technologies

Conclusions
This study developed a model to With a lower volume of CAVs and human-driven vehicles, the performance of PLOS without signal system would be better than with signal system. For the potential conflicts between pedestrians and vehicles, when the speed is higher, the number would be higher and impact the value of LOS. From the regression analysis, the volume of human-driven vehicles has the most contribution to the value of LOS.
The results of this study have contributed to understanding the impact of CAVs on pedestrians during the process of the CAVs' application development.
And this model can provide a reference to researchers and governments to consider coexistence factors related to CAVs in their future work.

Merits and Demerits
There are two significant advancements reported in this paper that are original.
First, using VISSIM and SSAM, a successful application of CAVs is demonstrated and to evaluate the impact of CAVs on pedestrians. The PLOS by comparing different proportions of CAVs in a traffic system were estimated. This will pro-Journal of Transportation Technologies vide a reference when dealing with the coexistence factors between CAVs and pedestrians.
Since the factors that influence the PLOS are complex and include traffic characteristics, further research on signal control system and coordination methods between CAVs and traditional vehicles need to be done. The regression model

Recommendations and Future Work
In future research, more parameters with the geometric design can be selected for a more comprehensive model. And the evaluation model in this paper could be extended to include the bicyclists. This extension of effort would allow the impact of CAVs on bicyclists to be evaluated using the same method.