scholarly journals Dynamic Bus Scheduling of Multiple Routes Based on Joint Optimization of Departure Time and Speed

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yingxin Liu ◽  
Xinggang Luo ◽  
Shengping Cheng ◽  
Yang Yu ◽  
Jiafu Tang
Keyword(s):  
Waiting Time ◽  
Traffic Congestion ◽  
Rational Solution ◽  
Urban Traffic ◽  
Joint Optimization ◽  
Passenger Flow ◽  
Bus Scheduling ◽  
Departure Time ◽  
Speed Group ◽  
Multiple Routes

Dynamic bus scheduling is a rational solution to the urban traffic congestion problem. Most previous studies have considered a single bus line, and research on multiple bus lines remains limited. Departure schedules have been typically planned by making separate decisions regarding departure times. In this study, a joint optimization model of the bus departure time and speed scheduling is constructed for multiple routes, and a coevolutionary algorithm (CEA) is developed with the objective function of minimizing the total waiting time of passengers. Six bus lines are selected in Shenyang, with several transfer stations between them, as a typical case. Experiments are then conducted for high-, medium-, and low-intensity case of smooth, increasing and decreasing passenger flow. The results indicate that combining the scheduling departure time and speed produces better performances than when using only scheduling departure time. The total passengers waiting time of the genetic algorithm (GA) group was reduced by approximately 25%–30% when compared to the fixed speed group. The total passengers waiting time of the CEA group can be reduced by approximately 17%–24% when compared to that in the GA group, which also holds true for a multisegment convex passenger flow. The feasibility and efficiency of the constructed algorithm were demonstrated experimentally.

scholarly journals Deep Wide Spatial-Temporal Based Transformer Networks Modeling for the Next Destination According to the Taxi Driver Behavior Prediction

2020 ◽  
Vol 11 (1) ◽  
pp. 17
Author(s):  
Zain Ul Abideen ◽  
Heli Sun ◽  
Zhou Yang ◽  
Rana Zeeshan Ahmad ◽  
Adnan Iftekhar ◽  
...  
Keyword(s):  
Waiting Time ◽  
Traffic Congestion ◽  
Human Mobility ◽  
Driver Behavior ◽  
Urban Traffic ◽  
Urban Transport ◽  
Geographical Information ◽  
Taxi Driver ◽  
Intelligent Transport System ◽  
Neural Network Approach

This paper uses a neural network approach transformer of taxi driver behavior to predict the next destination with geographical factors. The problem of predicting the next destination is a well-studied application of human mobility, for reducing traffic congestion and optimizing the electronic dispatching system’s performance. According to the Intelligent Transport System (ITS), this kind of task is usually modeled as a multi-class problem. We propose the novel model Deep Wide Spatial-Temporal-Based Transformer Networks (DWSTTNs). In our approach, the encoder and decoder are the transformer’s primary units; with the help of Location-Based Social Networks (LBSN), we encode the geographical information based on visited semantic locations. In particular, we trained our model for the exact longitude and latitude coordinates to predict the next destination. The benefit in the real world of this kind of research is to reduce the customer waiting time for a ride and driver waiting time to pick up a customer. Taxi companies can also optimize their management to improve their company’s service, while urban transport planner can use this information to better plan the urban traffic. We conducted extensive experiments on two real-word datasets, Porto and Manhattan, and the performance was improved compared to the previous models.

scholarly journals Agent-Based Modeling and Simulation for the Bus-Corridor Problem in a Many-to-One Mass Transit System

2014 ◽  
Vol 2014 ◽  
pp. 1-16
Author(s):  
Qinmu Xie ◽  
Shoufeng Ma ◽  
Ning Jia ◽  
Yang Gao
Keyword(s):  
Traffic Congestion ◽  
Evolutionary Dynamics ◽  
Mass Transit ◽  
Urban Traffic ◽  
Peak Period ◽  
Transit System ◽  
Departure Time ◽  
Departure Time Choice ◽  
Multiagent Modeling ◽  
Formation And Evolution

With the growing problem of urban traffic congestion, departure time choice is becoming a more important factor to commuters. By using multiagent modeling and the Bush-Mosteller reinforcement learning model, we simulated the day-to-day evolution of commuters’ departure time choice on a many-to-one mass transit system during the morning peak period. To start with, we verified the model by comparison with traditional analytical methods. Then the formation process of departure time equilibrium is investigated additionally. Seeing the validity of the model, some initial assumptions were relaxed and two groups of experiments were carried out considering commuters’ heterogeneity and memory limitations. The results showed that heterogeneous commuters’ departure time distribution is broader and has a lower peak at equilibrium and different people behave in different pattern. When each commuter has a limited memory, some fluctuations exist in the evolutionary dynamics of the system, and hence an ideal equilibrium can hardly be reached. This research is helpful in acquiring a better understanding of commuter’s departure time choice and commuting equilibrium of the peak period; the approach also provides an effective way to explore the formation and evolution of complicated traffic phenomena.

scholarly journals Population Density or Populations Size. Which Factor Determines Urban Traffic Congestion?

2021 ◽  
Vol 13 (8) ◽  
pp. 4280
Author(s):  
Yu Sang Chang ◽  
Sung Jun Jo ◽  
Yoo-Taek Lee ◽  
Yoonji Lee
Keyword(s):  
Population Density ◽  
Population Size ◽  
Traffic Congestion ◽  
Critical Role ◽  
Small Population ◽  
High Density ◽  
Urban Traffic ◽  
Contradictory Result ◽  
Low Density

A large number of articles have documented that as population density of cities increases, car use declines and public transit use rises. These articles had a significant impact of promoting high-density compact urban development to mitigate traffic congestion. Another approach followed by other researchers used the urban scaling model to indicate that traffic congestion increases as population size of cities increases, thus generating a possible contradictory result. Therefore, this study examines the role of both density and population size on traffic congestion in 164 global cities by the use of Stochastic Impacts by Regression on Population, Affluence and Technology model. We divide 164 cities into the two subgroups of 66 low density cities and 98 high density cities for analysis. The findings from the subgroups analysis indicated a clear-cut difference on the critical role of density in low-density cities and the exclusive role of population size in high-density cities. Furthermore, using threshold regression model, 164 cities are divided into the two regions of large and small population cities to determine population scale advantage of traffic congestion. Our findings highlight the importance of including analysis of subgroups based on density and/or population size in future studies of traffic congestion.

Collaborative passenger flow control of urban rail transit network considering balanced distribution of passengers

2021 ◽  
pp. 2150461
Author(s):  
Xiang Li ◽  
Yan Bai ◽  
Kaixiong Su
Keyword(s):  
Flow Control ◽  
Programming Model ◽  
Flow Characteristics ◽  
Service Level ◽  
Urban Traffic ◽  
Urban Rail Transit ◽  
Control Measures ◽  
Rail Transit ◽  
Passenger Flow ◽  
Urban Rail

The increase of urban traffic demands has directly affected some large cities that are now dealing with more serious urban rail transit congestion. In order to ensure the travel efficiency of passengers and improve the service level of urban rail transit, we proposed a multi-line collaborative passenger flow control model for urban rail transit networks. The model constructed here is based on passenger flow characteristics and congestion propagation rules. Considering the passenger demand constraints, as well as section transport and station capacity constraints, a linear programming model is established with the aim of minimizing total delayed time of passengers and minimizing control intensities at each station. The network constructed by Line 2, Line 6 and Line 8 of the Beijing metro is the study case used in this research to analyze control stations, control durations and control intensities. The results show that the number of delayed passengers is significantly reduced and the average flow control ratio is relatively balanced at each station, which indicates that the model can effectively relieve congestion and provide quantitative references for urban rail transit operators to come up with new and more effective passenger flow control measures.

scholarly journals Identification and Prediction of Urban Traffic Congestion via Cyber-Physical Link Optimization

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 63268-63278 ◽  
Author(s):  
Darong Huang ◽  
Zhenping Deng ◽  
Shaohua Wan ◽  
Bo Mi ◽  
Yang Liu
Keyword(s):  
Traffic Congestion ◽  
Urban Traffic

scholarly journals Discovering Urban Traffic Congestion Propagation Patterns With Taxi Trajectory Data

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 69481-69491 ◽  
Author(s):  
Zhenhua Chen ◽  
Yongjian Yang ◽  
Liping Huang ◽  
En Wang ◽  
Dawei Li
Keyword(s):  
Traffic Congestion ◽  
Urban Traffic ◽  
Trajectory Data ◽  
Taxi Trajectory

scholarly journals Research on Decision Evaluation Model of HOV Lane Setting

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ding Lv ◽  
Qunqi Wu ◽  
Bo Chen ◽  
Yahong Jiang
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Evaluation Model ◽  
Path Selection ◽  
Urban Traffic ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Travel Efficiency ◽  
Hov Lanes ◽  
The City

In order to achieve the purpose of improving the travel efficiency of commuters in the periphery of the city, expanding the beneficiary groups of urban rail transit, and alleviating urban road traffic congestion, when planning and setting up HOV in the periphery of the city, it is necessary to analyze the feasibility of HOV lane setting from both the demand conditions and the setting conditions. This paper combines machine learning to construct a decision-making evaluation model for HOV lane setting and studies the optimal layout model and algorithm of HOV lanes in service rail transit commuter chain. The setting, planning, and layout of HOV lanes are a two-way interactive process of traveler's path selection and designer's road planning. Finally, after the model is constructed, the performance of the system model is verified. The results show that the system studied in this paper can be used for traffic data and lane planning analysis. Therefore, in the process of urban operation, the HOV model constructed in this paper is mainly used to alleviate urban traffic and improve urban operation efficiency.

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