Modeling and Evaluation of the Impact of Motorcycles Mobility on Vehicular Traffic

Traffic simulation can help to evaluate the impact of different mobility beha-viors on the traffic flow from safety, efficiency, and environmental views. The objective of this paper is to extend the SUMO (Simulation of Urban Mobility) road traffic simulator to model and evaluate the impact of motorcycles mobility on vehicular traffic. First, we go through diverse mobility aspects and models for motorcycles in SUMO. Later, we opt for the most suitable mobility models of motorcycles. Finally, the impact of motorcycle mobility on different kinds of vehicles is investigated in terms of environment, fuel consumption, velocity and travel time. The result of modeling and evaluation shows that based on the mobility model of the motorcycle, vehicular traffic flow can be enhanced or deteriorated.


Introduction
There have been many efforts to enhance the mobility modeling of vehicles in order to assess or replicate specific traffic conditions such as jams or crashes. With the advent of intelligent transport systems, numerous novel applications have been proposed concerning vehicular modeling in terms of both individual and overall traffic flow. In this context, due to the growing number of motorcycles in many countries, we must take into account the significance of motorcycles' and other two wheelers' role in vehicular mobility.
The improvement of traffic efficiency is a worldwide problem that many gov- traffic. Based on their size and abilities in mobility, motorcycles sometimes do not follow the same physical traffic rules as other vehicles. For example, they can accelerate or decelerate faster, maneuver between lanes or in a shared lane, move to adjacent lanes, or even form dense traffic on an unsaturated road. Thereby, they can affect traffic jams, efficiency, safety and congestion [1].
Nowadays, since the number of vehicle types is increasing, conducting research and studying on traffic flow issues is one of the interesting topics for many companies. Based on the problems of ineffective traffic flows, companies like Google, TomTom, HERE Technologies are hugely investing to solve the problems related to traffic flows and provide a better service for their customers.
To simulate different mobility models, we have benefited from the open-source traffic simulation framework SUMO. Simulation is recognized to be one of the most beneficial tools for analyzing traffic flows. For instance, it would be very difficult, expensive and dangerous to set a traffic jam test in the real world. So, simulation of traffic is very cost-effective and helpful for studying traffic flows [2]. Here, the motorcycle mobility is modeled and its impact on the distinct types of vehicles traveling on the road is studied. This might be useful for many companies working on traffic flow issues. To this end, six different mobility models for motorcycles are devised and the impact of these models is explored on traffic flows. Different driving scenarios result in different traffic flows, speed, travel time, fuel consumption and emission of CO 2 .
The remainder of this paper is organized as follows. Section two deals with concepts concerning modeling motorcycle mobility. In the next section, we discuss simulation platform parameters and requirements. The results are evaluated in section four, where the impact of six mobility models for motorcycles is analyzed from efficiency and environmental aspects. Finally, a conclusion and future work are provided in the last section.

Mobility Modelling for Motorcycles
This section addresses the basic concepts of mobility modeling for motorcycles. Some paradigms such as lane changing models, car following and emission models are described in detail and the most appropriate parameters will be selected for modeling. Later, six different mobility models (sublane, lane change, normal, acceleration and deceleration, incident, and density) are presented in the form of six scenarios.
Each scenario contains some specific parameters. Acceleration and deceleration values, length, width and other parameters of each vehicle type are simulated based on measurements and values that are available in the SUMO website [3].
Lane-changing models, car-following models and emission classes are explained as follows.   [6]. Fifteen car following models are available on the SUMO website [3]. The three most popular car-following models are as follows:  Krauss (the default car-following model of SUMO).  This method is closer to the behavior of a driver in the real world [5].
The Wiedemann model is a psycho-physical distancing model. If a faster vehicle is approaching a slower leading vehicle, it will start to decelerate until it reaches its own threshold [2].
The information about speed and acceleration of the front vehicle are used in the Krauss and Wiedemann models which are usually difficult for a real driver to be calculated. This means that the Krauss and Wiedemann models react quicker than a human driver based on the acceleration and speed of the front vehicle. On the other hand, IDM calculates the speed based on the distance to the leading vehicle which is closer to the behavior of a driver in the real world [2] [5]. Since the IDM model is closer to the real world and a human driver behavior, it is the carfollowing model considered in our research.  Emission Models: SUMO includes the following emission models [3] defining its own emission classes:   [3].
Considering the mentioned topics, six distinct scenarios are proposed as follows to model various driving patterns of motorcycles on the road:  Sublane: One of the unique characteristics of motorcycles is their ability to share or overtake a vehicle in the same lane. The width of each lane is four meters (which is the standard width of one lane in Germany). To achieve the mentioned capability, each lane is divided into two 2-meter sublanes so that motorcycles can ride beside another vehicle in a shared lane.  Lane Change: Motorcycles can easily change lane due to their particular dynamics and structure. Lane change command is sent to the motorcycles at a specific point in time to study the behavior of vehicular traffic accordingly.  Normal: This acts a reference for other scenarios such that their results are to be evaluated with respect to the scenario. In the normal scenario, vehicles are inserted and travel on the road normally without any special maneuver or diversion.  Acceleration and Deceleration: As one of the most important mobility features, motorcycles can accelerate or decelerate irregularly and suddenly. Therefore, to simulate this behavior, we let them accelerate and decelerate twice at two different times and positions on the road to discover to what extent they influence the mobility of other road users.  Incident: In order to represent and analyze the impact of traffic jams caused by motorcycles on other vehicles, we made an occurrence happen to some of the motorcycles on the road and they stop in the middle of the route.  Density: To show the impact of motorcycles density on vehicular traffic, motorcycles are forced to travel slower than other vehicles in the middle of the route in this scenario. This issue will affect the mobility of other vehicles and create congestion on the road.

Implementation
To simulate different scenarios of traffic flows and evaluate the impact of motorcycles on traffic flows, SUMO has been used. It has been developed by the German Aerospace Center (DLR) [3] and is available since 2001 allowing the simu-  Figure 1.
SUMO supports the programming languages Java, Python and C++. The Traffic Control Interface (TraCI) is one of the useful tools of SUMO which is used to send commands to the vehicles and manipulate their maneuvers when they are traveling on the route. TraCI uses a TCP-based connection to provide access to SUMO which acts as a server that is started with an additional command line.
In the implementation, TraCI has been used to control the mobility of vehicles especially in incident and lane change scenarios. SUMO only prepares the simulation and waits for external applications to connect and take over the control.
After its start, clients connect to it by setting up a TCP connection to the appointed SUMO port. The client application sends commands to SUMO to control the simulation execution, influence single vehicle's behavior or to ask for environmental details. SUMO answers with a status-response to each command and additional results that depend on the given command. For example, in the incident and change lane scenario, a stop command and a changing lane command will be generated and sent through TraCI to the according vehicles.
In the simulation, four types of vehicles are considered. In total, 243 vehicles departed and traveled on the route. The detailed number of each vehicle type can be seen in Table 1. The length of the route is one kilometer including three edges (three parts) so that a more detailed investigation can be performed.
Here, our simulation contains some XML files for each scenario, one configuration file and python codes. By running the python codes, the SUMO software

Average Travel Time
The average travel time is obtained based on the average travel time of each

Average CO2 Emission
As already discussed in the paper, each type of vehicle takes advantage of its own In terms of car and motorcycle, they incurred less air pollution than heavy  vehicles. The cars' average CO 2 emission was nearly 0.75 kg for all mobility models except for the acceleration/deceleration case where the emission slightly fell to around 0.50 kg. In addition, motorcycles showed the same behavior as cars in terms of CO 2 emission apart from the sublane scenario where they emitted considerably more pollution than cars, roughly 1.5 times with almost 1.25 kg of CO 2 .
To sum up, it is derived that sublane and lane change scenarios had no particular impact on neither heavy nor low weight vehicles. The only exception is for motorcycles in the sublane model where their emission substantially climbed.
Besides, other mobility models especially acceleration/deceleration for both cars and motorcycles and density for trucks and buses demonstrated to have reduced the ecological impact.

Average Fuel Consumption
The calculation of the average fuel consumption is akin to the calculation of the  As it can be seen in Figure 5, in comparison to the normal scenario, the average fuel consumption remained nearly unchanged through sublane and lane change models for all vehicles excluding motorcycles in the sublane scenario that consumed significantly more fuel. In all mobility models, the average fuel consumption diminished modestly in the acceleration/deceleration scenario where it grew faintly in the incident model before it fell drastically in the density scenario for heavy vehicles and a little for other vehicles.

Conclusions and Future Work
In this paper, the impact of motorcycles mobility on different classes of vehicles in six scenarios was investigated. Various mobility models of motorcycles and de- vehicles and in density model for heavy weighted ones. Therefore, it is concluded that depending on the mobility model of the motorcycle, vehicular traffic flow can be enhanced or deteriorated. In future work, we aim to incorporate pedestrian mobility to examine the impact of another vulnerable road user on vehicular traffic.