Travel Time Enabled Bus Route Navigation System Experiment in Beijing

Author(s):  
Bo Liu ◽  
Wenjia Wang
Author(s):  
Stephen Steffes ◽  
Stephan Theil ◽  
Malak Samaan ◽  
Michael Conradt

Author(s):  
Ryo Takeno ◽  
Yosuke Seki ◽  
Masahiko Sano ◽  
Kenji Matsuura ◽  
Kenji Ohira ◽  
...  

2011 ◽  
Vol 20 (04) ◽  
pp. 753-781
Author(s):  
KAI CHEN ◽  
KIA MAKKI ◽  
NIKI PISSINOU

In the metropolitan region, most congestion or traffic jams are caused by the uneven distribution of traffic flow that creates bottleneck points where the traffic volume exceeds the road capacity. Additionally, unexpected incidents are the next most probable cause of these bottleneck regions. Moreover, most drivers are driving based on their empirical experience without awareness of real-time traffic situations. This unintelligent traffic behavior can make the congestion problem worse. Prediction based route guidance systems show great improvements in solving the inefficient diversion strategy problem by estimating future travel time when calculating accurate travel time is difficult. However, performances of machine learning based prediction models that are based on the historical data set degrade sharply during a congestion situation. This paper develops a new navigation system for reducing travel time of an individual driver and distributing the flow of urban traffic efficiently in order to reduce the occurrence of congestion. Compared with previous route guidance systems, the results reveal that our system, applying the advanced multi-lane prediction based real-time fastest path (AMPRFP) algorithm, can significantly reduce the travel time especially when drivers travel in a complex route environment and face frequent congestion problems. Unlike the previous system,1 it can be applied either for single lane or multi-lane urban traffic networks where the reason for congestion is significantly complex. We also demonstrate the advantages of this system and verify the results using real highway traffic data and a synthetic experiment.


Author(s):  
Kunimitsu Fujita ◽  
Masaya Kato ◽  
Tatsuya Furukane ◽  
Keiji Shibata ◽  
Yuukou Horita

ICTE 2015 ◽  
2015 ◽  
Author(s):  
Yuxi He ◽  
Renjie Du ◽  
Tong Zou ◽  
Nian Zhang ◽  
Xunfei Gao

Author(s):  
Tomohisa Yamashita ◽  
Koichi Kurumatani

With maturation of ubiquitous computing technology, it has become feasible to design new systems to improve our urban life. In this chapter, the authors introduce a new application for car navigation in a city. Every car navigation system in operation today has the current position of the vehicle, the destination, and the currently chosen route to the destination. If vehicles in a city could share this information, they could use traffic information to improve traffic efficiency for each vehicle and the whole system. Therefore, this chapter proposes a cooperative car navigation system with route information sharing (RIS). In the RIS system, each vehicle transmits route information (current position, destination, and route to the destination) to a route information server, which estimates future traffic congestion using current congestion information and this information and feeds its estimate back to each vehicle. Each vehicle uses the estimation to re-plan their route. This cycle is then repeated. The authors’ purpose in this chapter is to confirm the effectiveness of the proposed cooperative car navigation system with multiagent simulation. To evaluate the effect of the RIS system, we introduce two indexes; individual incentive and social acceptability. In theor traffic simulation with three types of road networks, the authors observe that the average travel time of the drivers using the RIS system is substantially shorter than the time of other drivers. Moreover, as the number of the RIS drivers increases, the average travel time of all drivers decreases. As a result of simulation, this chapter confirms that a cooperative car navigation system with the RIS system generally satisfied individual incentive and social acceptability, and had a effect for the improvement of traffic efficiency.


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