Topic
Taxis
About: Taxis is a research topic. Over the lifetime, 878 publications have been published within this topic receiving 13932 citations. The topic is also known as: directed movement in response to stimulus & GO:0042330.
Papers published on a yearly basis
1 / 2
Papers
2 Nov 2010
TL;DR: This paper mine smart driving directions from the historical GPS trajectories of a large number of taxis, and provides a user with the practically fastest route to a given destination at a given departure time.
Abstract: GPS-equipped taxis can be regarded as mobile sensors probing traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge. In this paper, we mine smart driving directions from the historical GPS trajectories of a large number of taxis, and provide a user with the practically fastest route to a given destination at a given departure time. In our approach, we propose a time-dependent landmark graph, where a node (landmark) is a road segment frequently traversed by taxis, to model the intelligence of taxi drivers and the properties of dynamic road networks. Then, a Variance-Entropy-Based Clustering approach is devised to estimate the distribution of travel time between two landmarks in different time slots. Based on this graph, we design a two-stage routing algorithm to compute the practically fastest route. We build our system based on a real-world trajectory dataset generated by over 33,000 taxis in a period of 3 months, and evaluate the system by conducting both synthetic experiments and in-the-field evaluations. As a result, 60-70% of the routes suggested by our method are faster than the competing methods, and 20% of the routes share the same results. On average, 50% of our routes are at least 20% faster than the competing approaches.
1,238 citations
TL;DR: In this paper, the authors explore who uses ridesourcing and for what reasons, how the ridesourcing market compares to that of traditional taxis, and how ridesourcing impacts the use of public transit and overall vehicle travel.
1,107 citations
TL;DR: In this article, the authors examined the efficiency of ride sharing services vis-a-vis taxis by comparing the capacity utilization rate of UberX drivers with that of traditional taxi drivers in five cities.
Abstract: In most cities, the taxi industry is highly regulated and utilizes technology developed in the 1940s. Ride sharing services such as Uber and Lyft, which use modern internet-based mobile technology to connect passengers and drivers, have begun to compete with traditional taxis. This paper examines the efficiency of ride sharing services vis-a-vis taxis by comparing the capacity utilization rate of UberX drivers with that of traditional taxi drivers in five cities. The capacity utilization rate is measured by the fraction of time a driver has a fare-paying passenger in the car while he or she is working, and by the share of total miles that drivers log in which a passenger is in their car. The main conclusion is that, in most cities with data available, UberX drivers spend a significantly higher fraction of their time, and drive a substantially higher share of miles, with a passenger in their car than do taxi drivers. Four factors likely contribute to the higher capacity utilization rate of UberX drivers: 1) Uber’s more efficient driver-passenger matching technology; 2)the larger scale of Uber than taxi companies; 3) inefficient taxi regulations; and 4) Uber’s flexible labor supply model and surge pricing more closely match supply with demand throughout the day.
807 citations
TL;DR: A recommender system for both taxi drivers and people expecting to take a taxi, using the knowledge of passengers' mobility patterns and taxi drivers' picking-up/dropping-off behaviors learned from the GPS trajectories of taxicabs to provide taxi drivers with some locations toward which they are more likely to pick up passengers quickly.
Abstract: This paper presents a recommender system for both taxi drivers and people expecting to take a taxi, using the knowledge of 1) passengers' mobility patterns and 2) taxi drivers' picking-up/dropping-off behaviors learned from the GPS trajectories of taxicabs. First, this recommender system provides taxi drivers with some locations and the routes to these locations, toward which they are more likely to pick up passengers quickly (during the routes or in these locations) and maximize the profit of the next trip. Second, it recommends people with some locations (within a walking distance) where they can easily find vacant taxis. In our method, we learn the above-mentioned knowledge (represented by probabilities) from GPS trajectories of taxis. Then, we feed the knowledge into a probabilistic model that estimates the profit of the candidate locations for a particular driver based on where and when the driver requests the recommendation. We build our system using historical trajectories generated by over 12,000 taxis during 110 days and validate the system with extensive evaluations including in-the-field user studies.
499 citations
TL;DR: In this article, the authors showed that autonomous taxis could reduce transport emissions by 87-94% per mile in 2030 and save approximately 7 billion barrels of oil, by moving passengers without human intervention.
Abstract: Autonomous vehicles move passengers without human intervention. Modelling suggests that autonomous taxis could reduce transport emissions by 87–94% per mile in 2030 and save approximately 7 billion barrels of oil.
429 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2026 | 1 |
| 2025 | 99 |
| 2024 | 175 |
| 2023 | 266 |
| 2022 | 468 |
| 2021 | 78 |