2016Advances in Mechanical EngineeringOpen access

Real-time bus travel speed estimation model based on bus GPS data

Jiancheng Weng, Chang Wang, Hainan Huang, Yueyue Wang, Ledian Zhang

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Abstract

Bus travel speed is the fundamental indicator for the bus operation dynamic monitoring, traveler’s information service, as well as the bus service evaluation. This article proposed a real-time travel speed calculation model for public transit based on global positioning system data. Combining the real-time bus global positioning system data with the bus geographical information system map, the study developed a series of processing procedures to match the bus location onto the map accurately, so that it could determine the bus position of the given arc and estimate the bus arrival time. Based on the estimated travel time and the actual distance between bus stops, a bus travel speed calculation model was established. In addition, the study designed a survey to verify the precision of the model. The error analysis results showed that the average precision of the travel speed between bus stops and the line speed estimation models were 88.4% and 97.9%, respectively. Finally, optimization strategies for the models were also made by keeping the stable high-frequency global positioning system data and modifying the stop position, and the precision of travel speed estimation was improved to 91.4%, which can meet the demand of the monitoring and evaluation of public transit operation.

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What this paper is about

Bus travel speed is the fundamental indicator for the bus operation dynamic monitoring, traveler’s information service, as well as the bus service evaluation. This article proposed a real-time travel speed calculation model for public transit based on global positioning system data. Combining the real-time bus global positioning system data with the bus geographical information system map, the study developed a series of processing procedures to match the bus location onto the map accurately, so that it could determine the bus position of the given arc and estimate the bus arrival time. Based on the estimated travel time and the actual distance between bus stops, a bus travel speed calculation model was established. In addition, the study designed a survey to verify the precision of the model. The error analysis results showed that the average precision of the travel speed between bus stops and the line speed estimation models were 88.4% and 97.9%, respectively. Finally, optimization strategies for the models were also made by keeping the stable high-frequency global positioning system data and modifying the stop position, and the precision of travel speed estimation was improved to 91.4%, which can meet the demand of the monitoring and evaluation of public transit operation.

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Available abstract

Bus travel speed is the fundamental indicator for the bus operation dynamic monitoring, traveler’s information service, as well as the bus service evaluation. This article proposed a real-time travel speed calculation model for public transit based on global positioning system data. Combining the real-time bus global positioning system data with the bus geographical information system map, the study developed a series of processing procedures to match the bus location onto the map accurately, so that it could determine the bus position of the given arc and estimate the bus arrival time. Based on the estimated travel time and the actual distance between bus stops, a bus travel speed calculation model was established. In addition, the study designed a survey to verify the precision of the model. The error analysis results showed that the average precision of the travel speed between bus stops and the line speed estimation models were 88.4% and 97.9%, respectively. Finally, optimization strategies for the models were also made by keeping the stable high-frequency global positioning system data and modifying the stop position, and the precision of travel speed estimation was improved to 91.4%, which can meet the demand of the monitoring and evaluation of public transit operation.

Key concepts: Global Positioning System, Public transport, Real-time computing, Computer science, Bus rapid transit, Position (finance), Automatic vehicle location, Transit (satellite)

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