With the development of location-based services and Big data technology, vehicle map matching techniques are growing rapidly, which is the fundamental techniques in the study of exploring global positioning system (GPS) data. The pre-processed GPS data can provide the guarantee of high-quality data for the research of mining passenger's points of interest and urban computing services. The existing surveys mainly focus on map-matching algorithms, but there are few descriptions on the key phases of the acquisition of sampling data, floating car and road data preprocessing in vehicle map matching systems. To address these limitations, the contribution of this survey on map matching techniques lies in the following aspects: (i) the background knowledge, function and system framework of vehicle map matching techniques; (ii) description of floating car data and road network structure to understand the detailed phase of map matching; (iii) data preprocessing rules, specific methodologies, and significance of floating car and road data; (iv) map matching algorithms are classified by the sampling frequency and data information. The authors give the introduction of open-source GPS sampling data sets, and the evaluation measurements of map-matching approaches; (v) the suggestions on data preprocessing and map matching algorithms in the future work. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Non-coordinated first-train timetables can result in unfavorable train connections among different subway lines at transfer stations and generate rather long waiting time for transfer passengers. This paper aims at optimizing the first train originating times of different transit lines at the network scale. The cost function of transfer waiting time is formulated to evaluate the perceived transfer quality with the consideration of passengers' psychological feelings. The first-train timetable coordination model is then developed by minimizing the total waiting cost of passengers transferring between two first trains of different lines. The genetic algorithm is applied to solve the model. Finally, a case study from part of the Beijing subway network is conducted to verify the method. The results show that the total waiting cost of first-train transfer passengers is reduced by 49.67% with the application of our proposed model. Meanwhile, the number of super-long waiting is significantly reduced.
Abstract:Theoretically speaking, the data of a stated preference survey could be suggested for the calibration of a stochastic route choice model. However, it is unrealistic to implement the questionnaire survey for such a large number of alternative routes. Engineers generally determine the parameter empirically. This experienced choice of perception parameter may cause higher errors in the route flows. In our calibration model of the perception parameter, the data of the cellular network is set as the input. This model consists of two levels. The upper level is to minimize the gap squares of the route choice ratio between the C-logit model and the cellular network data. The stochastic user equilibrium (SUE) in terms of the C-logit model is used as the lower level. The simulated annealing (SA) algorithm is used to solve the model, where the route-based gradient projection (GP) algorithm is used to solve the inner SUE. A case study is used to validate the convergence of the model calibration. A real-world road network is used to demonstrate the objective advantage of an equilibrium constraint over a nonequilibrium constraint and explain the feasibility of the candidate routes assumption.
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