Route Choice of the Shortest Travel Time Based on Floating Car Data

Jingwei Shen,Yifang Ban

doi:10.1155/2016/7041653

Jingwei Shen, Yifang Ban

Open Access

https://doi.org/10.1155/2016/7041653

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Abstract

Finding a route with shortest travel time according to the traffic condition can help travelers to make better route choice decisions. In this paper, the shortest travel time based on FCD (floating car data) which is used to assess overall traffic conditions is proposed. To better fit FCD and road map, a new map matching algorithm which fully considers distance factor, direction factor, and accessibility factor is designed to map all GPS (Global Positioning System) points to roads. A mixed graph structure is constructed and a route analysis algorithm of shortest travel time which considers the dynamic edge weight is designed. By comparing with other map matching algorithms, the proposed method has a higher accuracy. The comparison results show that the shortest travel time path is longer than the shortest distance path, but it costs less traveling time. The implementation of the route choice based on the shortest travel time method can be used to guide people’s travel by selecting the space-time dependent optimal path.

Highlights

There is an urgent need to obtain the traffic dynamics in a city for traffic guidance
Traffic information on road networks can be collected by induction loops or visual systems, it is difficult to obtain an accurate estimation of the instantaneous travel time from the local traffic speed and flow data [1]
According to the improved Dijkstra algorithm, the route choice prototype is developed. Both the shortest distance path and the shortest travel time path function are implemented. Since they may produce different results, the same starting point and end point are chosen for route analysis in the following three experiments

Summary

Introduction

There is an urgent need to obtain the traffic dynamics in a city for traffic guidance. Queries of the type “how do we get traffic information?” and “which path is the shortest distance between two vertices in a graph?” are widely addressed, while queries of the type “how do we get traffic information efficiently and economically?” and “which path is the shortest travel time between two vertices in a graph?” need further analysis. Traffic information on road networks can be collected by induction loops or visual systems, it is difficult to obtain an accurate estimation of the instantaneous travel time from the local traffic speed and flow data [1]. The spatiotemporal distribution of traffic congestions demonstrates a multinuclear structure in urban road networks [2]. With the availability of inexpensive positioning technology, it is possible to use historical navigation data to model the traffic flow at different times on a particular day

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