Dynamic control cycle speed limit strategy for balanced reduction of travel time and emissions

2021 ◽  
Vol 35 (09) ◽  
pp. 2150153
Author(s):  
Minghui Ma ◽  
Yaozong Zhang ◽  
Shidong Liang
Keyword(s):  
Travel Time ◽  
Traffic Control ◽  
Dynamic Control ◽  
Transmission Model ◽  
Cell Transmission Model ◽  
Vehicle Exhaust ◽  
Environmental Benefit ◽  
Multiple Control ◽  
Total Travel Time ◽  
Co Emission

The vehicle exhaust has been one of the major sources of greenhouse gas emissions. With an increase in traffic volume, it has been found that the introduced intelligent traffic control is necessary. This paper investigated a novel VSL strategy considering the dynamic control cycle to improve the traffic efficiency and environmental benefit on freeway. An extension of the cell transmission model (CTM) was used to depict the traffic characteristics under VSL control, and integrated with the microscopic emission and fuel consumption model VT-Micro to estimate the pollution emission of each cell. The VSL strategy was designed to provide multiple control cycles with different length to adjust the scope of VSL changes, furthermore, a probability formula was developed and used to determine the optimal quantity of control cycles to reduce the computational complexity of controller. An objective optimization function was formulated with the aim of minimizing total travel time and CO emission. With simulation experiments, the results showed that the proposed VSL strategy considering the dynamic control cycle outperformed uncontrolled scenario, resulting in up to 8.4% of total travel time reductions, 26.7% of delay optimization, and 14.5% reduction in CO emission, which enhanced the service level of freeway network.

scholarly journals A Distributed Traffic Control Strategy Based on Cell-Transmission Model

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 10771-10778 ◽  
Author(s):  
Pengfei Shao ◽  
Lei Wang ◽  
Wei Qian ◽  
Qing-guo Wang ◽  
Xu-Hua Yang
Keyword(s):  
Control Strategy ◽  
Traffic Control ◽  
Transmission Model ◽  
Cell Transmission Model ◽  
Cell Transmission

Determining Optimum Transit Signal Priority Implementation Locations on a Network

2020 ◽  
Vol 2674 (10) ◽  
pp. 387-400
Author(s):  
Murat Bayrak ◽  
S. Ilgin Guler
Keyword(s):  
Travel Time ◽  
Solution Method ◽  
Transmission Model ◽  
Transit Signal Priority ◽  
Passenger Travel ◽  
Total Travel Time ◽  
Network Operations ◽  
In Transit ◽  
Car Travel ◽  
Do So

Transit signal priority (TSP) can be used to improve bus operations at intersections. However, implementing TSP can often increase the delay of non-transit modes. Therefore, it is necessary to evaluate the effects of TSP both on car and bus operations to determine optimal locations to equip with TSP to improve network operations. To do so, the link transmission model is used to evaluate the travel times of both cars and buses on the network while accounting for dynamic queuing and queue spillover. This method is then used to evaluate different combinations of locations for TSP implementation and to determine the optimal configuration that can minimize the total travel time of network users, including bus and car passengers. The sensitivity of the proposed algorithm to demand level, changes in transit network, implementation strategy, and solution method are also evaluated. For all tested scenarios, the TSP configurations found to be optimum achieve a significant reduction of total bus passenger travel time while creating minimal effect on total car travel time. The results reveal that in general, not all intersections should be equipped with TSP, and intersections that carry high demand within a network are promising locations for TSP implementation to reduce the total travel time of network users. Additionally, it is found that the total travel time of network users can be further decreased by only activating TSP for buses with more than a certain number of on-board passengers.

ANALYSIS OF CELL TRANSMISSION MODEL FOR TRAFFIC FLOW SIMULATION WITH APPLICATION TO NETWORK TRAFFIC

2021 ◽  
pp. 1-16
Author(s):  
A. S. MAULANA ◽  
S. R. PUDJAPRASETYA
Keyword(s):  
Shock Waves ◽  
Travel Time ◽  
Traffic Flow ◽  
Finite Volume ◽  
Kinematic Model ◽  
Transmission Model ◽  
Finite Volume Scheme ◽  
Cell Transmission Model ◽  
Rarefaction Waves ◽  
Cell Transmission

Abstract The cell transmission model (CTM) is a macroscopic model that describes the dynamics of traffic flow over time and space. The effectiveness and accuracy of the CTM are discussed in this paper. First, the CTM formula is recognized as a finite-volume discretization of the kinematic traffic model with a trapezoidal flux function. To validate the constructed scheme, the simulation of shock waves and rarefaction waves as two important elements of traffic dynamics was performed. Adaptation of the CTM for intersecting and splitting cells is discussed. Its implementation on the road segment with traffic influx produces results that are consistent with the analytical solution of the kinematic model. Furthermore, a simulation on a simple road network shows the back and forth propagation of shock waves and rarefaction waves. Our numerical result agrees well with the existing result of Godunov’s finite-volume scheme. In addition, from this accurately proven scheme, we can extract information for the average travel time on a certain route, which is the most important information a traveller needs. It appears from simulations of different scenarios that, depending on the circumstances, a longer route may have a shorter travel time. Finally, there is a discussion on the possible application for traffic management in Indonesia during the Eid al-Fitr exodus.

Analysis of cell transmission model for traffic flow simulation with application to network traffic

2021 ◽  
Vol 63 ◽  
pp. 84-99
Author(s):  
A. S. Maulana ◽  
Sri Redjeki Pudjaprasetya
Keyword(s):  
Shock Waves ◽  
Travel Time ◽  
Traffic Flow ◽  
Finite Volume ◽  
Kinematic Model ◽  
Transmission Model ◽  
Finite Volume Scheme ◽  
Cell Transmission Model ◽  
Rarefaction Waves ◽  
Cell Transmission

The cell transmission model (CTM) is a macroscopic model that describes the dynamics of traffic flow over time and space. The effectiveness and accuracy of the CTM are discussed in this paper. First, the CTM formula is recognized as a finite-volume discretization of the kinematic traffic model with a trapezoidal flux function. To validate the constructed scheme, the simulation of shock waves and rarefaction waves as two important elements of traffic dynamics was performed. Adaptation of the CTM for intersecting and splitting cells is discussed. Its implementation on the road segment with traffic influx produces results that are consistent with the analytical solution of the kinematic model. Furthermore, a simulation on a simple road network shows the back and forth propagation of shock waves and rarefaction waves. Our numerical result agrees well with the existing result of Godunov’s finite-volume scheme. In addition, from this accurately proven scheme, we can extract information for the average travel time on a certain route, which is the most important information a traveller needs. It appears from simulations of different scenarios that, depending on the circumstances, a longer route may have a shorter travel time. Finally, there is a discussion on the possible application for traffic management in Indonesia during the Eid al-Fitr exodus.   doi:10.1017/S1446181121000080

Intelligent control of complex traffic conditions based on machine learning

2021 ◽  
pp. 1-12
Author(s):  
Zhe Li
Keyword(s):  
Machine Learning ◽  
Simulation Model ◽  
Traffic Simulation ◽  
Learning Algorithms ◽  
Control Model ◽  
Machine Learning Algorithms ◽  
Transmission Model ◽  
Cell Transmission Model ◽  
Traffic Conditions ◽  
Simulation Control

 In order to improve the simulation effect of complex traffic conditions, based on machine learning algorithms, this paper builds a simulation model. Starting from the macroscopic traffic flow LWR theory, this paper introduces the process of establishing the original CTM mathematical model, and combines it with machine learning algorithms to improve it, and establishes the variable cell transmission model VCTM ordinary transmission, split transmission, and combined transmission mathematical expressions. Moreover, this paper establishes a road network simulation model to calibrate related simulation parameters. In addition, this paper combines the actual needs of complex traffic conditions analysis to construct a complex traffic simulation control model based on machine learning, and designs a hybrid microscopic traffic simulation system architecture to simulate all relevant factors of complex road conditions. Finally, this paper designs experiments to verify the performance of the simulation model. The research results show that the simulation control model of complex traffic conditions constructed in this paper has certain practical effects.

Estimating Express Train Preference of Urban Railway Passengers Based on Extreme Gradient Boosting (XGBoost) using Smart Card Data

2021 ◽  
pp. 036119812110133
Author(s):  
Eun Hak Lee ◽  
Kyoungtae Kim ◽  
Seung-Young Kho ◽  
Dong-Kyu Kim ◽  
Shin-Hyung Cho
Keyword(s):  
Travel Time ◽  
Smart Card ◽  
Public Transportation ◽  
Gradient Boosting ◽  
Log Data ◽  
Smart Card Data ◽  
Extreme Gradient Boosting ◽  
Express Train ◽  
Total Travel Time ◽  
The Individual

As the share of public transport increases, the express strategy of the urban railway is regarded as one of the solutions that allow the public transportation system to operate efficiently. It is crucial to express the urban railway’s express strategy to balance a passenger load between the two types of trains, that is, local and express trains. This research aims to estimate passengers’ preference between local and express trains based on a machine learning technique. Extreme gradient boosting (XGBoost) is trained to model express train preference using smart card and train log data. The passengers are categorized into four types according to their preference for the local and express trains. The smart card data and train log data of Metro Line 9 in Seoul are combined to generate the individual trip chain alternatives for each passenger. With the dataset, the train preference is estimated by XGBoost, and Shapley additive explanations (SHAP) is used to interpret and analyze the importance of individual features. The overall F1 score of the model is estimated to be 0.982. The results of feature analysis show that the total travel time of the local train feature is found to substantially affect the probability of express train preference with a 1.871 SHAP value. As a result, the probability of the express train preference increases with longer total travel time, shorter in-vehicle time, shorter waiting time, and few transfers on the passenger’s route. The model shows notable performance in accuracy and provided an understanding of the estimation results.

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