scholarly journals Traffic flow cellular automaton model with bi-directional information in an open boundary condition

2020 ◽  
Author(s):  
Liang Zheng ◽  
Zhengpeng He
Keyword(s):  
Traffic Flow ◽  
Traffic Information ◽  
Open Boundary ◽  
Hysteresis Phenomenon ◽  
Connected Vehicle ◽  
Directional Information ◽  
Congested Traffic ◽  
Traffic Conditions ◽  
Ca Model ◽  
Vehicle Technologies

Abstract With Connected Vehicle Technologies being popular, drivers not only perceive downstream traffic information but also get upstream information by routinely checking backward traffic conditions, and the backward-looking frequency or probability is usually affected by prevailing traffic conditions. Meanwhile, the bi-directional perception range of drivers is expected to significantly increase, which results in more informed and coordinated driving behaviours. So, we propose a traffic flow bi-directional CA model with two perception ranges, and perform the numerical simulations with the field data collected from a one-lane highway in Richmond, California, USA as the benchmark data. Numerical results show that the CA model can effectively reproduce the oscillation of relatively congested traffic and the traffic hysteresis phenomenon. When adjusting the backward-looking probability and the perception range, the CA model can well simulate the travel times of all vehicles, and the generation and dissolution of traffic jams under various scenarios.

scholarly journals Controlling the Connected Vehicle with Bi-Directional Information: Improved Car-Following Models and Stability Analysis

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8322
Author(s):  
Ziwei Yi ◽  
Wenqi Lu ◽  
Xu Qu ◽  
Linheng Li ◽  
Peipei Mao ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Information Structure ◽  
Control Strategies ◽  
Open Boundary ◽  
Accurate Information ◽  
Connected Vehicle ◽  
Directional Information ◽  
Car Following ◽  
Car Following Model ◽  
Multiple Vehicles

Connected vehicle (CV) technologies are changing the form of traditional traffic models. In the CV driving environment, abundant and accurate information is available to vehicles, promoting the development of control strategies and models. Under these circumstances, this paper proposes a bidirectional vehicles information structure (BDVIS) by making use of the acceleration information of one preceding vehicle and one following vehicle to improve the car-following models. Then, we deduced the derived multiple vehicles information structure (DMVIS), including historical movement information of multiple vehicles, without the acceleration information. Next, the paper embeds the four kinds of basic car-following models into the framework to investigate the stability condition of two structures under the small perturbation of traffic flow and explored traffic response properties with different proportions of forward-looking or backward-looking terms. Under the open boundary condition, simulations on a single lane are conducted to validate the theoretical analysis. The results indicated that BDVIS and the DMVIS perform better than the original car-following model in improving the traffic flow stability, but that they have their own advantages for differently positioned vehicles in the platoon. Moreover, increasing the proportions of the preceding and following vehicles presents a benefit to stability, but if traffic is stable, an increase in any of the parameters would extend the influence time, which reveals that neither β1 or β2 is the biggest the best for the traffic.

USING VARIABLE SPEED LIMITS TO ELIMINATE WIDE MOVING JAMS: A STUDY BASED ON THREE-PHASE TRAFFIC THEORY

2012 ◽  
Vol 23 (09) ◽  
pp. 1250060 ◽  
Author(s):  
YIZHI WANG ◽  
YI ZHANG ◽  
JIANMING HU ◽  
LI LI
Keyword(s):  
Traffic Flow ◽  
Control Strategies ◽  
Vehicle Speed ◽  
Variable Speed ◽  
Speed Limits ◽  
Congested Traffic ◽  
Three Phase ◽  
Variable Speed Limits ◽  
Ca Model ◽  
Traffic Theory

One frequently observed congested traffic flow pattern is wide moving jam (WMJ), in which the average vehicle speed is very low and the density is very high. In some recent studies, variable speed limits (VSL) were proposed as effective measures to eliminate or abate the influence of jam waves. However, in most of these studies, the stochastic features of driving behaviors and the resulting uncertainty of traffic flow dynamics were not fully considered. In this paper, we use cellular automaton (CA) model-based simulations to test the performances of different VSL control strategies and apply the three-phase traffic theory to further analyze the obtained results. Based on the simulation results, we got two novel findings. Firstly, we observed seven, instead of the previously assumed six, states of traffic flow in the evolution process of WMJ, when VSL were applied. Secondly and more importantly, we found that inappropriate speed limit may induce new WMJ and exaggerate congestions in two ways: one way corresponds to an F → J transition and the other corresponds to an F → S → J transition. Based on these findings, the appropriate lower bound of VSL was finally discussed in this paper.

scholarly journals Development and application of the network weight matrix to predict traffic flow for congested and uncongested conditions

2018 ◽  
Vol 46 (9) ◽  
pp. 1684-1705 ◽  
Author(s):  
Alireza Ermagun ◽  
David M Levinson
Keyword(s):  
Traffic Flow ◽  
User Equilibrium ◽  
Weight Matrix ◽  
Congested Traffic ◽  
Traffic Forecasting ◽  
Traffic Conditions ◽  
Spatial Weight ◽  
Flow Information ◽  
Network Weight ◽  
Better Than

To capture network dependence between traffic links, we introduce two distinct network weight matrices ([Formula: see text]), which replace spatial weight matrices used in traffic forecasting methods. The first stands on the notion of betweenness centrality and link vulnerability in traffic networks. To derive this matrix, we use an unweighted betweenness method and assume all traffic flow is assigned to the shortest path. The other relies on flow rate change in traffic links. For forming this matrix, we use the flow information of traffic links and employ user equilibrium assignment and the method of successive averages algorithm to solve the network. The components of the network weight matrices are a function not simply of adjacency, but of network topology, network structure, and demand configuration. We test and compare the network weight matrices in different traffic conditions using the Nguyen–Dupuis network. The results lead to a conclusion that the network weight matrices operate better than traditional spatial weight matrices. Comparing the unweighted and flow-weighted network weight matrices, we also reveal that the assigned flow network weight matrices perform two times better than a betweenness network weight matrix, particularly in congested traffic conditions.

scholarly journals Cooperative Traffic Signal Control with Traffic Flow Prediction in Multi-Intersection

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 137 ◽  
Author(s):  
Daeho Kim ◽  
Okran Jeong
Keyword(s):  
Reinforcement Learning ◽  
Traffic Flow ◽  
Learning Algorithm ◽  
Traffic Signal ◽  
Traffic Information ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Flow Prediction ◽  
Traffic Conditions ◽  
Flow Prediction

As traffic congestion in cities becomes serious, intelligent traffic signal control has been actively studied. Deep Q-Network (DQN), a representative deep reinforcement learning algorithm, is applied to various domains from fully-observable game environment to traffic signal control. Due to the effective performance of DQN, deep reinforcement learning has improved speeds and various DQN extensions have been introduced. However, most traffic signal control researches were performed at a single intersection, and because of the use of virtual simulators, there are limitations that do not take into account variables that affect actual traffic conditions. In this paper, we propose a cooperative traffic signal control with traffic flow prediction (TFP-CTSC) for a multi-intersection. A traffic flow prediction model predicts future traffic state and considers the variables that affect actual traffic conditions. In addition, for cooperative traffic signal control in multi-intersection, each intersection is modeled as an agent, and each agent is trained to take best action by receiving traffic states from the road environment. To deal with multi-intersection efficiently, agents share their traffic information with other adjacent intersections. In the experiment, TFP-CTSC is compared with existing traffic signal control algorithms in a 4 × 4 intersection environment. We verify our traffic flow prediction and cooperative method.

Application of Trajectory Data for Investigating Vehicle Behavior in Mixed Traffic Environment

2018 ◽  
Vol 2672 (43) ◽  
pp. 122-133 ◽  
Author(s):  
Narayana Raju ◽  
Pallav Kumar ◽  
Aayush Jain ◽  
Shriniwas S. Arkatkar ◽  
Gaurang Joshi
Keyword(s):  
Traffic Flow ◽  
Research Work ◽  
Driving Behavior ◽  
Video Data ◽  
Hysteresis Phenomenon ◽  
Mixed Traffic ◽  
Trajectory Data ◽  
Traffic Conditions ◽  
Micro Level ◽  
Flow States

The research work reported here investigates driving behavior under mixed traffic conditions on high-speed, multilane highways. With the involvement of multiple vehicle classes, high-resolution trajectory data is necessary for exploring vehicle-following, lateral movement, and seeping behavior under varying traffic flow states. An access-controlled, mid-block road section was selected for video data collection under varying traffic flow conditions. Using a semi-automated image processing tool, vehicular trajectory data was developed for three different traffic states. Micro-level behavior such as lateral placement of vehicles as a function of speed, instant responses, vehicle-following behavior, and hysteresis phenomenon were evaluated under different traffic flow states. It was found that lane-wise behavior degraded with increase in traffic volume and vehicles showed a propensity to move towards the median at low flow and towards the curb-side at moderate and heavy flows. Further, vehicle-following behavior was also investigated and it was found that with increase in flow level, vehicles are more inclined to mimic the leader vehicle’s behavior. In addition to following time, perceiving time of subject vehicle for different leading vehicles was also evaluated for different vehicle classes. From the analysis, it was inferred that smaller vehicles are switching their leader vehicles more often to escape from delay, resulting in less following and perceiving time and aggressive gap acceptance. The present research work reveals the need for high-quality, micro-level data for calibrating driving behavior models under mixed traffic conditions.

Assessment of the Robustness of Signal Timing Plans in an Arterial Corridor Through Seasonal Variation of Traffic Flows

2017 ◽  
Vol 2619 (1) ◽  
pp. 85-94
Author(s):  
Marija Ostojic ◽  
Aleksandar Stevanovic ◽  
Dusan Jolovic ◽  
Hani S. Mahmassani
Keyword(s):  
Traffic Flow ◽  
New Technologies ◽  
The United States ◽  
Time Of Day ◽  
Traffic Information ◽  
Quality Data ◽  
Signal Timing ◽  
Traffic Demand ◽  
Traffic Conditions ◽  
Arterial Traffic

Arterial traffic signal systems, predominantly in the United States, deploy multiple signal timing plans to account for daily variability of traffic demand. Those types of traffic flow deviations should be anticipated when timing plans are designed and, therefore, serviced satisfactorily. When traffic flow patterns are no longer predictable, a predetermined time-of-day (TOD) plan may no longer be the optimal one. This research aimed to examine signal timing optimality by applying a method similar to the selection of a traffic responsive plan to recognize automatically the best timing plan suited to current traffic conditions. The proposed method attempted to determine whether the optimality of signal timing settings could have been effectively estimated when systematic detector counts of the major approach were available. The study used 4 months of data from field microwave detectors coupled with data of turning-movement counts obtained over several days. The findings show that TOD signal timing plans mainly depended on adequate data collection that best describes a specific set of traffic conditions. Thus, the designed plan was as optimal as the related traffic information was reliable, whereas a problem arose in the case of limited-availability and low-quality data. New technologies are capable of collecting and storing massive amounts of data. Even if the granularity of collected data is low, the data can be used to improve traffic performance (i.e., reduce corridor delay). This realization could be of particular importance to traffic agencies that have installed, or plan to install, new field devices.

scholarly journals Characteristics of Traffic Flow at Nonsignalized T-Shaped Intersection with U-Turn Movements

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Hong-Qiang Fan ◽  
Bin Jia ◽  
Xin-Gang Li ◽  
Jun-Fang Tian ◽  
Xue-Dong Yan
Keyword(s):  
Traffic Flow ◽  
Performance Measures ◽  
Initial Conditions ◽  
Main Road ◽  
Control Delay ◽  
Traffic Conditions ◽  
Ca Model ◽  
Average Control ◽  
Simulation Results ◽  
High Congestion

Most nonsignalized T-shaped intersections permit U-turn movements, which make the traffic conditions of intersection complex. In this paper, a new cellular automaton (CA) model is proposed to characterize the traffic flow at the intersection of this type. In present CA model, new rules are designed to avoid the conflicts among different directional vehicles and eliminate the gridlock. Two kinds of performance measures (i.e., flux and average control delay) for intersection are compared. The impacts of U-turn movements are analyzed under different initial conditions. Simulation results demonstrate that (i) the average control delay is more practical than flux in measuring the performance of intersection, (ii) U-turn movements increase the range and degree of high congestion, and (iii) U-turn movements on the different direction of main road have asymmetrical influences on the traffic conditions of intersection.

Smart Driving System for Improving Traffic Flow

2017 ◽  
Vol 7 (7) ◽  
pp. 236
Author(s):  
Rajesh Kumar Gupta ◽  
L. N. Padhy ◽  
Sanjay Kumar Padhi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Adaptive Cruise Control ◽  
Human Perception ◽  
Cruise Control ◽  
Driving System ◽  
V2v Communication ◽  
Traffic Conditions ◽  
Cooperative Adaptive Cruise Control

Traffic congestion on road networks is one of the most significant problems that is faced in almost all urban areas. Driving under traffic congestion compels frequent idling, acceleration, and braking, which increase energy consumption and wear and tear on vehicles. By efficiently maneuvering vehicles, traffic flow can be improved. An Adaptive Cruise Control (ACC) system in a car automatically detects its leading vehicle and adjusts the headway by using both the throttle and the brake. Conventional ACC systems are not suitable in congested traffic conditions due to their response delay.  For this purpose, development of smart technologies that contribute to improved traffic flow, throughput and safety is needed. In today’s traffic, to achieve the safe inter-vehicle distance, improve safety, avoid congestion and the limited human perception of traffic conditions and human reaction characteristics constrains should be analyzed. In addition, erroneous human driving conditions may generate shockwaves in addition which causes traffic flow instabilities. In this paper to achieve inter-vehicle distance and improved throughput, we consider Cooperative Adaptive Cruise Control (CACC) system. CACC is then implemented in Smart Driving System. For better Performance, wireless communication is used to exchange Information of individual vehicle. By introducing vehicle to vehicle (V2V) communication and vehicle to roadside infrastructure (V2R) communications, the vehicle gets information not only from its previous and following vehicle but also from the vehicles in front of the previous Vehicle and following vehicle. This enables a vehicle to follow its predecessor at a closer distance under tighter control.

Travel mode recognition of urban residents using mobile phone data and MapAPI

2021 ◽  
pp. 239980832098300
Author(s):  
Zhenghong Peng ◽  
Guikai Bai ◽  
Hao Wu ◽  
Lingbo Liu ◽  
Yang Yu
Keyword(s):  
Urban Planning ◽  
Mobile Phone ◽  
Traffic Flow ◽  
Road Traffic ◽  
Urban Traffic ◽  
Phone Call ◽  
Traffic Information ◽  
Mobile Phone Data ◽  
Urban Residents ◽  
Travel Mode

Obtaining the time and space features of the travel of urban residents can facilitate urban traffic optimization and urban planning. As traditional methods often have limited sample coverage and lack timeliness, the application of big data such as mobile phone data in urban studies makes it possible to rapidly acquire the features of residents’ travel. However, few studies have attempted to use them to recognize the travel modes of residents. Based on mobile phone call detail records and the Web MapAPI, the present study proposes a method to recognize the travel mode of urban residents. The main processes include: (a) using DBSCAN clustering to analyze each user’s important location points and identify their main travel trajectories; (b) using an online map API to analyze user’s means of travel; (c) comparing the two to recognize the travel mode of residents. Applying this method in a GIS platform can further help obtain the traffic flow of various means, such as walking, driving, and public transit, on different roads during peak hours on weekdays. Results are cross-checked with other data sources and are proven effective. Besides recognizing travel modes of residents, the proposed method can also be applied for studies such as travel costs, housing–job balance, and road traffic pressure. The study acquires about 6 million residents’ travel modes, working place and residence information, and analyzes the means of travel and traffic flow in the commuting of 3 million residents using the proposed method. The findings not only provide new ideas for the collection and application of urban traffic information, but also provide data support for urban planning and traffic management.

scholarly journals Safety improvements by intelligent connected vehicle technologies: A meta-analysis considering market penetration rates

2021 ◽  
Vol 159 ◽  
pp. 106234
Author(s):  
Guiming Xiao ◽  
Jaeyoung Lee ◽  
Qianshan Jiang ◽  
Helai Huang ◽  
Mohamed Abdel-Aty ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Connected Vehicle ◽  
Market Penetration ◽  
Vehicle Technologies
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