An effective method to estimate urban link travel time in real-time traffic information system
ABSTRACT Nowadays the floating car technology is playing a more and more important role in the dynamic route guidance, congestion management, traffic incidents detection, because it can collect more accurate travel time information in real-time traffic service systems. However the key problem of using it is that the moving direction and driving behaviors of floating car at low speed are dynamic and quite complex, disregarded these influence factors will seriously affect the accuracy of the evaluation of the average link travel time. In this paper, we proposed an effective average link travel time evolution method via combining trajectories with a new road network structure. Finally, we carried out our experiments on real data of 15,000 taxies for 13 months in several ways.
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ABSTRACT: This paper discusses an object-oriented neural network model that was developed for predicting short-term traffic conditions on a section of the Pacific Highway between Brisbane and the Gold Coast in Queensland, Australia. The feasibility of this approach is demonstrated through a time-lag recurrent network (TLRN) which was developed for predicting speed data up to 15 minutes into the future. The results obtained indicate that the TLRN is capable of predicting speed up to 5 minutes into the future with a high degree of accuracy (90–94%). Similar models, which were developed for predicting freeway travel times on the same facility, were successful in predicting travel times up to 15 minutes into the future with a similar degree of accuracy (93–95%). These results represent substantial improvements on conventional model performance and clearly demonstrate the feasibility of using the object-oriented approach for short-term traffic prediction.European Journal of Operational Research. 01/2001;
Conference Proceeding: Impact of Probe Vehicles Sample Size on Link Travel Time Estimation[show abstract] [hide abstract]
ABSTRACT: Many transportation management agencies are using probe to collect average link travel time, which is significant for congestion management, traffic incidents detection, routing guidance and traveler information systems. A key issue of using probe technologies is to determine the probe vehicles sample size required to collect traffic information cost-effectively. This paper proposed a method based on random sampling theory to analyze the impact of probe vehicles sample size on accuracy of average link travel time estimation without considering the population distribution of traffic data. Finally, case study was performed with simulation and field data. The analysis result shows that (1) time interval has little affect on the accuracy of ALTT estimation while probe sample size is fixed; (2) when probe vehicles sample size reaches to certain level, estimated errors reduce gently with the increasing of the number of probe vehicles. The transportation management agencies may determine the optimal probe sample size according to the errors confidence interval estimated by the proposed methodIntelligent Transportation Systems Conference, 2006. ITSC '06. IEEE; 10/2006
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ABSTRACT: The traditional map-matching algorithms mainly use two methods: the incremental method and the global method. Both of them have advantages and disadvantages of themselves: while the global map-matching algorithm produces better matching results, the incremental algorithm produces results of lower quality faster. All things considering the two traditional algorithms, this paper proposes a heuristic map-matching algorithm by using vector-based recognition. Firstly, the algorithm uses the heuristic search method which is similar to A* algorithm, and it makes use of geometric operation to form the restriction, and make the comparison between the vector formed with the vehicular GPS points and the special road network to heuristicly search and select the vehicular possible traveling routes. Secondly, it globally compares the vehicular every possible route by calculating the map-matching weight, and then chooses the optimal one. The result of testing demonstrates the efficiency of the algorithm both at accuracy and computational speed when handling the large-scale data of GPS tracking data even under the complex road network conditions.Computing in the Global Information Technology, International Multi-Conference on. 01/2007;