Name
Abstract | ||
|---|---|---|
Facing various uncertainties in real world traffic, navigation services are typically formulated as a certain stochastic shortest path problem (SSPP). In the past several years, many stochastic objectives in SSPP has been proposed, among which maximizing the probability of arrival on time draws certain attentions from researchers as it takes, besides absolute travel time, an extra dimension of information (user-specified deadlines) into consideration. This paper extends a recently proposed data-driven approach for SSPP in the following two aspects: (1) when deadline is loose enough, the original method may return more than one paths since all of them meet deadline with 100% probability. We propose to return a unique path with the least number of intersections by reformulating the objective function. (2) we extend the arrive-on-time problem to the case of visiting several fixed locations, which is a common scenario in real world application. Experimental results show the accuracy and effectiveness of the improved data-driven method. |
Year | Venue | Field |
|---|---|---|
2016 | 2016 IEEE 19TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) | Mathematical optimization,Shortest path problem,Simulation,Stochastic process,Minification,Linear programming,Travel time,Mathematics |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yaoxin Wu | 1 | 0 | 0.34 |
| Wei Chen | 2 | 0 | 0.34 |
| Xuexi Zhang | 3 | 6 | 2.78 |
| Guangjun Liao | 4 | 0 | 0.68 |