Name
Abstract | ||
|---|---|---|
Under a stochastic roadway, drivers need a route guidance system incorporating travel time variability. To recommend a customized path depending on the trip purpose and the driver’s risk-taking behavior, various path ranking methods have been developed. Unlike those methods, our proposed disutility method can easily incorporate a target arrival time in the ranking process by measuring how late the travel is and by penalizing it depending on the severity of lateness. In addition, the disutility-based route guidance system can properly address travel time unreliability that causes unacceptable disruptions to the driver’s schedule (i.e., unexpected long delay). We compare the disutility-based path ranking method with other ranking methods, the percentile travel time, the mean excess travel time, and the on-time arrival probability. We show that the disutility has stronger discriminating power and requires less solution space to find an optimal path. The most important advantage is that it can estimate a driver’s risk-taking behavior for each trip purpose by using the discrete choice analysis. We construct a simulation framework to acquire the travel time data on a hypothetical roadway. We analyze the data and show how various ranking methods recommend a customized path. Using the data, we show the advantage of the disutiltiy method over the other methods, which is generating a customized path with respect to the target arrival time by properly penalizing the travel time lateness. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1080/15472450.2018.1542303 | Journal of Intelligent Transportation Systems |
Keywords | Field | DocType |
Delay penalty cost,route guidance system,target arrival time,travel time disutility,travel time unreliability | Transport engineering,Guidance system,Engineering,Travel time | Journal |
Volume | Issue | ISSN |
23 | 3 | 1547-2450 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haengju Lee | 1 | 0 | 0.68 |
| Saerona Choi | 2 | 0 | 0.34 |
| Hojin Jung | 3 | 0 | 1.35 |
| Byungkyu Brian Park | 4 | 0 | 0.68 |
| Sang H. Son | 5 | 2433 | 202.20 |