Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining

In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are required to fulfill the given amount of trip demand. For this purpose, first, we develop a deep neural network model, called ST-NN (Spatio-Temporal Neural Network), to predict taxi trip time from the raw GPS trip data. Secondly, we develop a carpooling simulation environment for RL training, with the output of ST-NN and using the NYC taxi trip dataset. In order to maximize transportation efficiency and minimize traffic congestion, we choose the effective distance covered by the driver on a carpool trip as the reward. Therefore, the more effective distance a driver achieves over a trip (i.e. to satisfy more trip demand) the higher the efficiency and the less will be the traffic congestion. We compared the performance of RL learned policy to a fixed policy (which always accepts carpool) as a baseline and obtained promising results that are interpretable and demonstrate the advantage of our RL approach. We also compare the performance of ST-NN to that of state-of-the-art travel time estimation methods and observe that ST-NN significantly improves the prediction performance and is more robust to outliers.Comment: Accepted at IEEE International Conference on Big Data 2018. arXiv admin note: text overlap with arXiv:1710.0435

A Simple Baseline for Travel Time Estimation using Large-Scale Trip Data

The increased availability of large-scale trajectory data around the world provides rich information for the study of urban dynamics. For example, New York City Taxi Limousine Commission regularly releases source-destination information about trips in the taxis they regulate. Taxi data provide information about traffic patterns, and thus enable the study of urban flow -- what will traffic between two locations look like at a certain date and time in the future? Existing big data methods try to outdo each other in terms of complexity and algorithmic sophistication. In the spirit of "big data beats algorithms", we present a very simple baseline which outperforms state-of-the-art approaches, including Bing Maps and Baidu Maps (whose APIs permit large scale experimentation). Such a travel time estimation baseline has several important uses, such as navigation (fast travel time estimates can serve as approximate heuristics for A search variants for path finding) and trip planning (which uses operating hours for popular destinations along with travel time estimates to create an itinerary).Comment: 12 page

Quantifying the benefits of vehicle pooling with shareability networks

Taxi services are a vital part of urban transportation, and a considerable contributor to traffic congestion and air pollution causing substantial adverse effects on human health. Sharing taxi trips is a possible way of reducing the negative impact of taxi services on cities, but this comes at the expense of passenger discomfort quantifiable in terms of a longer travel time. Due to computational challenges, taxi sharing has traditionally been approached on small scales, such as within airport perimeters, or with dynamical ad-hoc heuristics. However, a mathematical framework for the systematic understanding of the tradeoff between collective benefits of sharing and individual passenger discomfort is lacking. Here we introduce the notion of shareability network which allows us to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets. We apply this framework to a dataset of millions of taxi trips taken in New York City, showing that with increasing but still relatively low passenger discomfort, cumulative trip length can be cut by 40% or more. This benefit comes with reductions in service cost, emissions, and with split fares, hinting towards a wide passenger acceptance of such a shared service. Simulation of a realistic online system demonstrates the feasibility of a shareable taxi service in New York City. Shareability as a function of trip density saturates fast, suggesting effectiveness of the taxi sharing system also in cities with much sparser taxi fleets or when willingness to share is low.Comment: Main text: 6 pages, 3 figures, SI: 24 page

New ITS applications for metropolitan areas based on Floating Car Data

The paper describes a couple of FCD based vehicular traffic applications and services. This new method is especially beneficial for regions with a poor traffic monitoring infrastructure because the necessary monetary effort to establish such a system is very small in comparison to conventional systems and it is flexible and easily adaptable to other regions. Particularly, emerging markets like China with a fast-changing road network and a high penetration of lat-est information technologies on one side but with serious foreseeable traffic related problems on the other side can surely profit from this approach. The new data collection and analysing methods result in better performance of the services enhance the scope of the services and hopefully enlarge user acceptance. All of the proposed solutions are prototypes and not all of them have been extensively tested up to now. Certainly, specific data processing methods need further research, some refinements and calibrations. Additionally, some applications still suffer from insufficient data penetration. Nevertheless, the approach is very general and it is very likely that FCD availability will sharply increase in near future and will enhance the quality of services

Birds of a Feather Flock Together: A Study of Doctor-Patient Matching

In this paper we study individuals' choice of general practitioners (GPs) utilizing revealed preferences data from the introduction of a regular general practitioner scheme in Norway. Having information on relevant travel distances, we compute decision makers' travel costs associated with different modes of travel. Choice probabilities are estimated by means of nested logit regression on a representative sample of Oslo inhabitants. The results support the general hypothesis that patients prefer doctors who resemble themselves on observable characteristics: Individuals prefer GPs having the same gender and similar age. Specialist status of GPs was found to have a smaller effect on choice probabilities than other attributes such as matching gender. When travel costs are calculated by means of taxi prices, the estimated willingness to pay for specialist status of a GP amounts to € 0.89 per consultation, whereas the estimated willingness to pay for having a GP with the same gender amounts to respectively € 1.71 and € 3.55 for female and male decision makers, respectively.GP services; Discrete choice; Willingness-to-pay; Health care demand