scholarly journals Building the Traffic Flow Network with Taxi GPS Trajectories and Its Application to Identify Urban Congestion Areas for Traffic Planning

2020 ◽  
Vol 13 (1) ◽  
pp. 266
Author(s):  
Jiayu Qin ◽  
Gang Mei ◽  
Lei Xiao
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Traffic Planning ◽  
Flow Network ◽  
Gps Trajectories ◽  
Congestion Problems

Traffic congestion is becoming a critical problem in urban traffic planning. Intelligent transportation systems can help expand the capacity of urban roads to alleviate traffic congestion. As a key concept in intelligent transportation systems, urban traffic networks, especially dynamic traffic networks, can serve as potential solutions for traffic congestion, based on the complex network theory. In this paper, we build a traffic flow network model to investigate traffic congestion problems through taxi GPS trajectories. Moreover, to verify the effectiveness of the traffic flow network, an actual case of identifying the congestion areas is considered. The results indicate that the traffic flow network is reliable. Finally, several key problems related to traffic flow networks are discussed. The proposed traffic flow network can provide a methodological reference for traffic planning, especially to solve traffic congestion problems.

scholarly journals SCENARIO MODEL TO REDUCE TRAFFIC CONGESTION USING INTELLIGENT TRANSPORTATION SYSTEMS

2020 ◽  
Vol 6 (2) ◽  
pp. 54-73
Author(s):  
Erma Suryani ◽  
◽  
Rully Agus Hendrawan ◽  
Fizar Syafa’at ◽  
Alifia Az-Zahra ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Scenario Model

Traffic flow guidance algorithm in intelligent transportation systems considering the effect of non-floating vehicle

2019 ◽  
Vol 23 (19) ◽  
pp. 9097-9110 ◽  
Author(s):  
Yu-Feng Chen ◽  
Zhan Gao ◽  
Hong Zhou ◽  
Yan Wang ◽  
Tao Zhang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Guidance Algorithm

INTELLIGENT TRANSPORTATION SYSTEMS IN IMPROVING TRAFFIC FLOW IN TOURISM DESTINATIONS∗

2007 ◽  
Vol 13 (3) ◽  
pp. 627-636
Author(s):  
Edna Mrnjavac ◽  
Robert Marsanić
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Emergency Services ◽  
Road Traffic ◽  
Weather Conditions ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Transportation Infrastructure ◽  
Public Traffic ◽  
Tourism Destinations

The rapid growth and development of motorisation combined with relatively small investments made to improving transportation infrastructure in cities, as well as in tourism destinations, has led to serious problems in the unobstructed movement of vehicles in public traffic areas. Traffic congestion on roadways, in ferryboat ports and at state borders during the summer months and year-round lines of cars going to or returning from work are a regular presence in traffic in most urban and tourism destinations in Croatia, as well as in the rest of Europe. Intelligent transportation systems (ITS) can be implemented in urban and tourism centres, which, for example, have no opportunity for increasing the capacity of their traffic networks by constructing new, or expanding existing, transportation infrastructure, and no opportunity for increasing parking capacities. The only solution would be to optimise traffic networking by introducing intelligent technologies. Intelligent transportation systems and services represent a coupling of information and telecommunication technologies with transportation means and infrastructure to ensure greater efficiency in the mobility of people and goods. ITS implementation helps to provide better information to motorists and travellers (tourists); improve traffic and tourist flows, cargo transportation, public passenger-transportation; facilitate the work of emergency services; enable electronic traffic-related payments; enhance the security of people in road traffic; and monitor weather conditions and the environment. To motorists the system provides guidance to roads on which traffic is less intense, guidance to available parking spaces, and guidance, for example, to a good restaurant or interesting tourist attraction. his paper focuses, in particular, on ITS application in city and tourism destinations in connection with parking problems. Guiding vehicles to the closest vacant parking space helps to reduce traffic congestion, reduce the amount of time lost in searching and increase the occupancy rate of car-parks

Analysis of the Influence of Traffic Information Messages on Route Selection

1995 ◽  
Vol 39 (17) ◽  
pp. 1082-1086
Author(s):  
W. Bradley Fain
Keyword(s):  
Situation Awareness ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Information ◽  
Route Selection ◽  
Variable Message Signs ◽  
Message Format ◽  
Message Signs

Intelligent Transportation Systems (ITS) can reduce traffic congestion by displaying congestion-related delay information on roadside variable message signs or in-vehicle displays. Message format and content may have a significant impact on the percentage of drivers who decide to make a route diversion. In this study, the effect of various traffic information message types on driver routing decisions was evaluated. Results suggest that messages including both an advisory and a descriptive component promote situation awareness and rapid decision making, both of which are critical for this application.

scholarly journals Forecasting Short-Term Traffic Flow by Fuzzy Wavelet Neural Network with Parameters Optimized by Biogeography-Based Optimization Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jeng-Fung Chen ◽  
Shih-Kuei Lo ◽  
Quang Hung Do
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Mean Squared Error ◽  
Transportation Systems ◽  
Wavelet Neural Network ◽  
Percentage Error ◽  
Short Term ◽  
Fuzzy Wavelet Neural Network

Forecasting short-term traffic flow is a key task of intelligent transportation systems, which can influence the traveler behaviors and reduce traffic congestion, fuel consumption, and accident risks. This paper proposes a fuzzy wavelet neural network (FWNN) trained by improved biogeography-based optimization (BBO) algorithm for forecasting short-term traffic flow using past traffic data. The original BBO is enhanced by the ring topology and Powell’s method to advance the exploration capability and increase the convergence speed. Our presented approach combines the strengths of fuzzy logic, wavelet transform, neural network, and the heuristic algorithm to detect the trends and patterns of transportation data and thus has been successfully applied to transport forecasting. Other different forecasting methods, including ANN-based model, FWNN-based model, and WNN-based model, are also developed to validate the proposed approach. In order to make the comparisons across different methods, the performance evaluation is based on root-mean-squared error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (R). The performance indexes show that the FWNN model achieves lower RMSE and MAPE, as well as higherR, indicating that the FWNN model is a better predictor.

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