Transit schedule reliability depends on realistic time estimates and timetable design. Manual schedule adjustment is labor-intensive and may not identify optimal solutions. This work is innovative because it combines statistical analysis of travel time patterns with optimization algorithms to generate transit schedules.
This paper addresses on-time performance optimization for fixed-schedule transit vehicles using unsupervised mechanisms. The work develops genetic algorithms and greedy approaches to generate schedules that maximize the probability of buses arriving within desired time windows. The methodology uses clustering to identify similar travel time patterns across different temporal periods.
The paper demonstrates genetic algorithm optimization improving average on-time performance from 62.9 percent to 74.7 percent on Nashville transit routes. Results show how clustering monthly travel patterns enables generation of better transit schedules that account for seasonal variations.
@inproceedings{Sun2017a,
author = {Sun, Fangzhou and Samal, Chinmaya and White, Jules and Dubey, Abhishek},
booktitle = {2017 {IEEE} International Conference on Smart Computing, {SMARTCOMP} 2017, Hong Kong, China, May 29-31, 2017},
title = {Unsupervised Mechanisms for Optimizing On-Time Performance of Fixed Schedule Transit Vehicles},
year = {2017},
acceptance = {37.5},
pages = {1--8},
abstract = {The on-time arrival performance of vehicles at stops is a critical metric for both riders and city planners to evaluate the reliability of a transit system. However, it is a non-trivial task for transit agencies to adjust the existing bus schedule to optimize the on-time performance for the future. For example, severe weather conditions and special events in the city could slow down traffic and cause bus delay. Furthermore, the delay of previous trips may affect the initial departure time of consecutive trips and generate accumulated delay. In this paper, we formulate the problem as a single-objective optimization task with constraints and propose a greedy algorithm and a genetic algorithm to generate bus schedules at timepoints that improve the bus on-time performance at timepoints which is indicated by whether the arrival delay is within the desired range. We use the Nashville bus system as a case study and simulate the optimization performance using historical data. The comparative analysis of the results identifies that delay patterns change over time and reveals the efficiency of the greedy and genetic algorithms.},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/smartcomp/SunSWD17},
category = {selectiveconference},
contribution = {lead},
doi = {10.1109/SMARTCOMP.2017.7947057},
file = {:Sun2017a-Unsupervised_Mechanisms_for_Optimizing_On-Time_Performance_of_Fixed_Schedule_Transit_Vehicles.pdf:PDF},
keywords = {transit scheduling, genetic algorithms, on-time performance, optimization, clustering},
project = {smart-transit,smart-cities},
tag = {ai4cps,transit},
timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
url = {https://doi.org/10.1109/SMARTCOMP.2017.7947057}
}