VISSIM-Based Simulation and Analysis of Upstream Segments in Ramp Areas for Optimizing Vehicle Group Lane-Changing Behaviors

Beijing Engineering Research Center of Urban Transportation Operation Support, Beijing University of Technology, Beijing 100124, China China Merchants New Intelligence Technology Co., Ltd., Beijing 100073, China National Virtual Experimental Teaching Center of Rail Traffic Communication and Control, Beijing Jiaotong University, Beijing 100044, China Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, China Academy of Transportation Sciences, Beijing 100029, China


Introduction
Traffic congestion has been a common problem all over the world and must be solved urgently in recent years. Ramp areas have become severe traffic congestion bottlenecks. Scholars at home and abroad seek to solve traffic congestion of ramp areas. With the application of advanced traffic control technologies and the progress of connected vehicle technologies, emerging technology has become one of the most effective ways of reducing traffic congestion in ramp areas. e main reason for the congestion in ramp areas is that the vehicles fail to change lanes in time. is causes the vehicles to change lanes only when they are close to the ramp area, which affects the normal operation of other vehicles. In the ramp area upstream segment, based on the study of vehicle group lane-changing behaviors, this paper continuously optimizes the lane-changing strategies of vehicle groups. At the same time, effective optimization models of ramp areas are established for reducing traffic congestion. ese lane-changing strategies by vehicle groups will reduce vehicle delays and increase the capacity in ramp areas. e gap acceptance theory was the basis of most previous lane-changing models, which shows whether a gap is accepted for lane-changing [1,2]. e lane-changing behaviors of vehicles can separate into three types at ramp merges: free lane-changing, cooperative lane-changing, and forced lanechanging [3,4]. Learning through lane-changing behaviors, the impacts of lane-changing on surrounding vehicles are identified. A lane-changing model is proposed for estimating the capacities of weaving segments [5]. Wang et al. [6] studied a hidden Markov model (HMM) method for the two-stage lane-changing model to more accurately indicate the forced lane-changing behaviors in urban arterials. e model is parameterized and evaluated on detailed vehicle trajectory data of next-generation simulation (NGSIM). Arbis and Dixit [7] employed a quantum response equilibrium framework to simulate the lane-changing maneuvers while considering conflict risks and estimated the model parameters based on a freeway on-ramp lane-changing data using the NGSIM dataset. It was determined that the likelihood of conflict could be substantially reduced to add longer acceleration lanes to reduce on-ramps speed limits. Deng and Feng [8] proposed a lane-changing decision model of multiattribute that is focused on an analytic hierarchy process and then presented a newly improved multilane traffic cellular automata model. According to numerical simulations under various traffic densities, the variations in the lane-line-markings affected the lane-changing cause chances and the lane-changing success chances. Zhou et al. [9] introduced a hyperbolic-tangent lane-changing trajectory model that is given a large number of reference angle data. e model is evaluated on real data and via simulations. e results demonstrate that drivers' lane-changing trajectories could successfully be described based on the proposed lane-change trajectory model.
In recent years, connected vehicle technologies have been widely valued by scholars all over the world. e application of connected vehicle technologies in the field of transportation has had a substantial impact on vehicle driving behaviors. e University of Maryland focused on Traffic View, which is a traffic monitoring device that is based on vehicular ad hoc networks. Using mobile ad hoc networks and car-to-car communication, a vehicle communication platform was used to collect and to publish traffic information to improve traffic safety and efficiency [10]. Collaborative adaptive cruise control (CACC) at the University of California contributed to the improvement of connected vehicle technologies. e stability and speed of traffic flow in platoon form were realized; acceleration and spacing information was transmitted in real time. It required the fleet to establish a complete communication system and required improvements to the existing vehicle longitudinal control algorithm [11]. Sharma et al. [12] incorporated driver compliance behavior into a connected vehicle driving strategy (CVDS). e driver obedience was modeled based on prospect theory (PT) and the connected vehicle trajectory data were utilized to correct CVDS, which was combined with an intellectual driver model. e demarcated results showed that drivers could drive safely and efficiently under the connected environment. Ye and Yamamoto [13] presented the distributions of acceleration and velocity difference in mixed traffic flow. It was determined that the dynamics of mixed traffic flow evolve with the increase of the penetration rate of connected and autonomous vehicles (CAV). e results demonstrated that with the increase of the CAV popularization rate, traffic safety had been significantly improved. Dai et al. [14] studied the connection between a traffic system performance and the driver's path choice under a connected vehicle environment. e dynamic travel of commuter's daily path choice in the connected vehicle environment was investigated. At the same time, real-time information and driving experience were regarded. Yao et al. [15][16][17] focused on dynamic platoon discrete models in the traffic detection environment of a cross section. e model can forecast the arrival of vehicles and optimize traffic signals continuously. e effectiveness of the proposed method was verified by modeling the actual road network in VISSIM. Li et al. [18] presented a new car-following model for platoon control of a multivehicle system under a V2V/ V2I communication environment. Qi et al. [19,20] described the cell transmission model of traffic flow based on urban road networks. e results could be used to estimate the impact of driving behaviors on traffic delay and to perfect the role of the state in the prevention of urban traffic congestion accidents. As our previous work, Li et al. [21] used VISSIM to simulate six scenarios to obtain the optimal vehicle group lane-changing behaviors for upstream traffic flow in off-ramp areas. As the complexity and multiformity of vehicle group lane-changing behaviors, more scenarios and parameters should be tested.
Ramp area and weaving area control strategies have been deeply and continuously researched by scholars at home and abroad. e study of ramp areas has mainly followed three stages [22]. Early studies focused on improving transport facilities and the traffic organization was optimized via reasonable channelization [23]. e second stage was based on research on the dynamic management of variable information boards or other systems, which could reduce the mainline delay by controlling the ramp traffic flow [24]. In the third state, car-road coordination was investigated to realize vehicle active intervention measures to improve the operational efficiency of ramp areas and weaving areas. A control strategy for ramps was proposed in the scenario of both intelligent vehicles and ordinary vehicles [25]. Marinescu et al. [26] presented a slot-driven merging algorithm. e results demonstrated that the algorithm realized low delay and very high volume on an on-ramp. Sivaraman et al. [27] used an automotive tested instrument with sensors to monitor key areas around the vehicle. e system recommended the suitable acceleration or deceleration to merge into the neighboring lane and assigned when and how to merge. Tanaka et al. [28] proposed vehicle control algorithms to prevent weaving conflicts from happening. A microscopic traffic simulation model achieved developed algorithms. e results demonstrated that the total throughput decreased slowly with the increase of the weaving ratio. Huang et al. [29] studied the relationship between off-ramp lane-changing spacing and traffic congestions on urban expressways and tested the impact of different densities of traffic and off-ramp ratios on the congestion. e results showed that with the increase of density of traffic and the proportion of off-ramp vehicles, the required lane-changing spacing should be increased accordingly. Zhao et al. [30,31] presented a comprehensive design model to remove weaving and to maximize the enhancement of the capacity of the section. An et al. [32] established a cellular automata model considering three different regulations of lane-changing to mate with the driving behaviors in lane distribution.
Currently, the research on-ramps modeling at home and abroad mainly was based on the specific traffic environment and lane-changing behaviors of independent vehicles. ere are few studies on the lane-changing behavior of vehicle groups. With the realization of connected vehicle technologies, the ramp traffic model can be based on the studies of the lane-changing behavior of vehicle groups. In a connected vehicle environment, it is necessary to conduct suitable modeling and analysis of ramp areas. erefore, this paper studies the behaviors of vehicle group lane-changing in the upstream sections of ramps.
e simulation results are validated by VISSIM. By leading and restricting the vehicle group lane-changing behaviors, the ramp capacities are improved.

Modeling and Method
With the continuous improvement of the connected vehicle technologies, drivers can obtain real-time traffic information for surrounding roads in a connected vehicle environment, including the state data of adjacent vehicles, the environment, and road facilities, for conducting vehicle trajectory planning and safety early warning for vehicle groups and for realizing lane-changing driving. Based on a connected vehicle environment, this paper establishes lane-changing of vehicle group models and strategies in the upstream sections of ramp areas. e optimal lane-changing strategy is ob-   Journal of Advanced Transportation q out � q 1 r 1 + q 2 r 2 + q 3 r 3 , According to its value, p can be divided into three cases: p � 0, p � 1, and 0 < p < 1.  is paper uses the common three-lane section to study the unified lanechanging strategies of vehicle groups in the upstream section in a ramp area.
(1)  Journal of Advanced Transportation outer lane, the original through-moving vehicles change to inner lanes as vehicle groups to keep the equilibrium among the lanes. In the upstream section in the ramp area, vehicles of off-ramp change lanes in advance. After entering the ramp area, these vehicles can exit the ramp smoothly, thereby avoiding affecting the vehicles of the mainline.

e Stepped Lane-Changing of Vehicle Group Strategies.
In    Table 1. Table 2 presents the lane-changing schematic diagrams of simulation plans. e corresponding lane-changing strategies in all simulation scenarios are listed in Table 1 and Table 2 presents schematic diagrams that correspond to Table 1. An off-ramp ratio 20% is used for all lane-changing plans. e lane-changing paths for vehicle lane-changing are determined by the "p" value. In the schematic diagram column of Table 2, the corresponding lane-changing space is shown in color, in which the slant filling of the area

Simulation and Analysis
800 m 400 m A4 800 m 800 m

A5
(400 + 400) m 400 m represents the lane-changing decision space with the plan. e oblique filling in the region represents the lanechanging decision space in the scheme. e schematic diagrams differ among plans due to differences according to the lane-changing decision space and the lane-changing space.  Table 3 is the summary of output results. To eliminate the influence of accidental factors, each scenario is simulated many times, and the average value of the results is taken. When the traffic volume output does not satisfy the requirement, these results will not be used as the basis for analysis; the best plan of output value is marked in bold in Table 3.

Results and Discussion
According to the simulation plans that are shown in Table 1, the simulation results are presented in Table 3. From Table 3 Figure 5(a) is a histogram of the delay results that correspond to Table 3. Plan A4 is the optimum simulation results and the mean delay value is the smallest among all plans. If the traffic flow requirement is satisfied, according to the comparison of plans A1 to A4 with flows of 1000 pcu/ h/lane and 1200 pcu/h/lane, only increasing the lanechanging space without lane-changing in advance has no effect on reducing the delay; instead, it increases it. If the lane-changing space is increased, vehicles should be guided to complete lane-changing as quickly as possible so as to change lanes ahead of the off-ramp, which will substantially reduce the vehicle delay and alleviate the traffic congestion.
To compare the effects of unified lane-changing and stepped lane-changing in terms of the vehicle group lanechanging behaviors, in the condition of meeting the traffic outputs, plan A4 is selected for comparison with plans A6, A7, and A8 at flows of 1000 pcu/h/lane and 1200 pcu/h/lane. According to Figure 5

Conclusions
To improve the capacity of the road and to alleviate the ramp area congestion, this paper studies the vehicle group lanechanging in the upstream sections of ramps. By optimizing the vehicle group lane-changing strategies, vehicles can be guided to pass ramp areas quickly and smoothly. With the continuous improvement of technologies of connected vehicles, lane-changing behavior coordination of vehicle group can be able to guide and restrain driving behaviors; then, congestion and delays have been effectively reduced in ramp areas. In an environment of connected vehicles, this paper proposes lane-changing strategies of vehicle groups and simulates the proposed strategies via VISSIM. Finally, the optimum lane-changing strategy is determined. is paper studies the vehicle group lane-changing behaviors in the upstream section of a ramp area; it provides an efficacious managerial approach to solve the problem of traffic congestion in ramp areas. In addition, it provides a basis for using the collaborative constraint method to analyze the vehicle group lane-changing behaviors in ramp areas.
According to the research results of this paper, the following research focuses are as follows: (1)

Conflicts of Interest
e authors declare that there are no conflicts of interest regarding the publication of this paper.