Abstract
Urban crowdsourced transportation, which can solve traffic problem within city, is a new scenario where citizens share vehicles to take passengers and packages while driving. Differing from the traditional location based crowdsourcing system (e.g., crowdsensing system), the task has to be completed with visiting two different locations (i.e., start and end points), so task allocation algorithms in crowdsensing cannot be leveraged in urban crowdsourced transportation directly. To solve this problem, we first prove that maximizing the crowdsourcing system’s profit (i.e., maximizing the total saved distance) is an NP-hard problem. We propose a heuristic greedy algorithm called Saving Most First (SMF) which is simple and effective in assigning tasks. Then, an optimized SMF based genetic algorithm (SMF-GA) is devised to jump out of the local optimal result. Finally, we demonstrate the performance of SMF and SMF-GA with extensive evaluations, based on a large scale real vehicle traces. The evaluation with large scale real dataset indicates that both SMF and SMF-GA algorithms outperform other benchmark algorithms in terms of saved distance, participant profits, etc.
Highlights
Transportation exists everywhere; a busy traffic has become a basic feature of large prosperous cities
It has been proved that some heuristic methods can help solve the above problem, so we propose the Saving Most First based genetic algorithm (SMF-GA) to make our result as close as the optimal solution
To evaluate the effectiveness of SMF-GA algorithm, a large scale real world dataset is used in this paper
Summary
Transportation exists everywhere; a busy traffic has become a basic feature of large prosperous cities. The traffic congestion has become a headache problem for people thanks to the large amount of vehicles on the road, bad planed city, etc. Mexico City, which has the worst traffic condition in the world, makes drivers cost nearly 97% extra time during the morning hours to arrive their destinations [2]. Even though in Los Angeles which has a good urban planning, a driver can hit a traffic jam at a 40% rate. How to accommodate such large amount of cars on roads at the same time is really a vexed problem for urban planners
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