Informed driving is increasingly becoming a key feature for increasing the sustainability of taxi companies. The sensors that are installed in each vehicle are providing new opportunities for automatically discovering knowledge, which, in return, delivers information for real-time decision making. Intelligent transportation systems for taxi dispatching and for finding time-saving routes are already exploring these sensing data. This paper introduces a novel methodology for predicting the spatial distribution of taxi-passengers for a short-term time horizon using streaming data. First, the information was aggregated into a histogram time series. Then, three time-series forecasting techniques were combined to originate a prediction. Experimental tests were conducted using the online data that are transmitted by 441 vehicles of a fleet running in the city of Porto, Portugal. The results demonstrated that the proposed framework can provide effective insight into the spatiotemporal distribution of taxi-passenger demand for a 30-min horizon.

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Predicting Taxi–Passenger Demand Using Streaming Data

VANETs have emerged as an exciting research and application area. Increasingly vehicles are being equipped with embedded sensors, processing and wireless communication capabilities. This has opened a myriad of possibilities for powerful and potential life-changing applications on safety, efficiency, comfort, public collaboration and participation, while they are on the road. Although, considered as a special case of a Mobile Ad Hoc Network, the high but constrained mobility of vehicles bring new challenges to data communication and application design in VANETs. This is due to their highly dynamic and intermittent connected topology and different application's QoS requirements. In this work, we survey VANETs focusing on their communication and application challenges. In particular, we discuss the protocol stack of this type of network, and provide a qualitative comparison between most common protocols in the literature. We then present a detailed discussion of different categories of VANET applications. Finally, we discuss open research problems to encourage the design of new VANET solutions.

https://hal.archives-ouvertes.fr/hal-01369972/document

Data communication in VANETs

We proposed and developed a taxi-sharing system that accepts taxi passengers’ real-time ride requests sent from smartphones and schedules proper taxis to pick up them via ridesharing, subject to time, capacity, and monetary constraints. The monetary constraints provide incentives for both passengers and taxi drivers: passengers will not pay more compared with no ridesharing and get compensated if their travel time is lengthened due to ridesharing; taxi drivers will make money for all the detour distance due to ridesharing. While such a system is of significant social and environmental benefit, e.g., saving energy consumption and satisfying people's commute, real-time taxi-sharing has not been well studied yet. To this end, we devise a mobile-cloud architecture based taxi-sharing system. Taxi riders and taxi drivers use the taxi-sharing service provided by the system via a smart phone App. The Cloud first finds candidate taxis quickly for a taxi ride request using a taxi searching algorithm supported by a spatio-temporal index. A scheduling process is then performed in the cloud to select a taxi that satisfies the request with minimum increase in travel distance. We built an experimental platform using the GPS trajectories generated by over 33,000 taxis over a period of three months. A ride request generator is developed (available at http://cs.uic.edu/∼sma/ridesharing) in terms of the stochastic process modelling real ride requests learned from the data set. Tested on this platform with extensive experiments, our proposed system demonstrated its efficiency, effectiveness and scalability. For example, when the ratio of the number of ride requests to the number of taxis is 6, our proposed system serves three times as many taxi riders as that when no ridesharing is performed while saving 11 percent in total travel distance and 7 percent taxi fare per rider.

/pdf/real-time-city-scale-taxi-ridesharing-33uotracyq.pdf

Real-Time City-Scale Taxi Ridesharing

Vehicles equipped with GPS localizers are an important sensory device for examining people’s movements and activities. Taxis equipped with GPS localizers serve the transportation needs of a large number of people driven by diverse needs; their traces can tell us where passengers were picked up and dropped off, which route was taken, and what steps the driver took to find a new passenger. In this article, we provide an exhaustive survey of the work on mining these traces. We first provide a formalization of the data sets, along with an overview of different mechanisms for preprocessing the data. We then classify the existing work into three main categories: social dynamics, traffic dynamics and operational dynamics. Social dynamics refers to the study of the collective behaviour of a city’s population, based on their observed movements; Traffic dynamics studies the resulting flow of the movement through the road network; Operational dynamics refers to the study and analysis of taxi driver’s modus operandi. We discuss the different problems currently being researched, the various approaches proposed, and suggest new avenues of research. Finally, we present a historical overview of the research work in this field and discuss which areas hold most promise for future research.

From taxi GPS traces to social and community dynamics: A survey

http://romisatriawahono.net/lecture/rm/survey/pervasive%20computing/Castro%20-%20From%20Taxi%20GPS%20Traces%20to%20Social%20and%20Community%20Dynamics%20-%202013.pdf

17 From Taxi GPS Traces to Social and Community Dynamics: A Survey

Traffic congestion is a serious problem in urban areas of many countries due to the increasing amount of vehicles on surface streets and accompanies fuel-wasting and air-pollution. This problem can be alleviated by adopting a ride-sharing service. The main idea of ride-sharing service is to collect travelers whose travel destinations are nearby into one vehicle. In doing so, we can reduce the amount of vehicles on surface streets and meantime save fuel. In this paper, we focus on the taxi-sharing service and propose a dynamic taxi-sharing system based on Intelligent Transportation Systems (ITS) technology. In the proposed system, we can immediately serve each irregular ride-sharing request and find a fuel-saving taxi for it. The simulation results show that our solution can exactly select a fuel-saving taxi for each ride-sharing request and outperform in responding time, the number of compared taxis, and fuel-saving while comparing with existing solutions.

A Fuel-Saving and Pollution-Reducing Dynamic Taxi-Sharing Protocol in VANETs

A dynamic taxi-sharing system and a sharing method thereof. A ride-sharing service provider carries at least one passenger from the first current location to the first travel destination through a route in an area and sends registration information to a service server. A plurality of ride-sharing requests is sent to the service server, and each of the ride-sharing requests comprises the second current location and the second travel destination. The service server divides the area into n by n blocks. A ride-matching module of the service server chooses a destination area in which the first travel destination is a center point and divides the destination area into k by k blocks. According to the registration information and the ride-sharing requests, the ride-matching module matches at least one ride-sharing traveler to the ride-sharing service provider to achieve a goal of fuel-saving and pollution-reducing.

Je-Wei Liu

Papers

A Fuel-Saving and Pollution-Reducing Dynamic Taxi-Sharing Protocol in VANETs

Dynamic Taxi-Sharing System and Sharing Method Thereof