Using mobility information to perform a feasibility study and the evaluation of spatio-temporal energy demanded by an electric taxi fleet

Half of the global population already lives in urban areas, facing to the problem of air pollution mainly caused by the transportation system. The recently worsening of urban air quality has a direct impact on the human health. Replacing today’s internal combustion engine vehicles with electric ones in public fleets could provide a deep impact on the air quality in the cities. In this paper, real mobility information is used as decision support for the taxi fleet manager to promote the adoption of electric taxi cabs in the city of San Francisco, USA. Firstly, mobility characteristics and energy requirements of a single taxi are analyzed. Then, the results are generalized to all vehicles from the taxi fleet. An electrificability rate of the taxi fleet is generated, providing information about the number of current trips that could be performed by electric taxis without modifying the current driver mobility patterns. The analysis results reveal that 75.2% of the current taxis could be replaced by electric vehicles, considering a current standard battery capacity (24–30 kWh). This value can increase significantly (to 100%), taking into account the evolution of the price and capacity of the batteries installed in the last models of electric vehicles that are coming to the market. The economic analysis shows that the purchasing costs of an electric taxi are bigger than conventional one. However, fuel, maintenance and repair costs are much lower. Using the expected energy consumption information evaluated in this study, the total spatio-temporal demand of electric energy required to recharge the electric fleet is also calculated, allowing identifying optimal location of charging infrastructure based on realistic routing patterns. This information could also be used by the distribution system operator to identify possible reinforcement actions in the electric grid in order to promote introducing electric vehicles

Modeling Taxi Drivers' Behaviour for the Next Destination Prediction

In this paper, we study how to model taxi drivers' behaviour and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey. Predicting the next location is a well studied problem in human mobility, which finds several applications in real-world scenarios, from optimizing the efficiency of electronic dispatching systems to predicting and reducing the traffic jam. This task is normally modeled as a multiclass classification problem, where the goal is to select, among a set of already known locations, the next taxi destination. We present a Recurrent Neural Network (RNN) approach that models the taxi drivers' behaviour and encodes the semantics of visited locations by using geographical information from Location-Based Social Networks (LBSNs). In particular, RNNs are trained to predict the exact coordinates of the next destination, overcoming the problem of producing, in output, a limited set of locations, seen during the training phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge 2015 dataset - based on the city of Porto -, obtaining better results with respect to the competition winner, whilst using less information, and on Manhattan and San Francisco datasets.Comment: preprint version of a paper submitted to IEEE Transactions on Intelligent Transportation System

IDENTIFYING AREA HOTSPOTS AND TAXI PICKUP TIMES USING SPATIAL DENSITY-BASED CLUSTERING

Taxis are one of the competitive sectors of transportation and are recognized as convenient and easy means of transportation to meet individual needs. However, in the operation of a taxi there are some problems that would make the taxi service less optimal, such as the difficulty with finding a taxi at specific hours, the imbalance between demand and taxi supplies, and the length of passengers waiting for a taxi. Therefore, to optimize taxi service, a knowledge base is needed for strategic management decision making. In the study, data of exploration taxis uses a DBSCAN algorithm aimed at identifying and clustering pickup hotspots based on time during weekday and weekend time from Queens, New York City. As for the features used which are pickup latitude and pickup longitude. Accuracy scores for modeling use coefficients to achieve accuracy scores of 0.80 on weekdays and 0.77 on weekends where the accuracy score falls into the accurate category in modeling. Results show that there are three areas of taxi pickup centers based on high taxi demand in January 2016, where they are at LaGuardia airport, John f. Kennedy international, and the area around Steinway Street

An Ant-based Intelligent Design for Future Self-driving Commercial Car Service Strategy

The technology of self-driving cars will inevitably change the industry of taxis and ride-sharing cars that provide important commercial ground transportation services to travelers, tourists and local residents. There is no doubt that new techniques, business models and strategies will be needed to follow the use of self-driving cars. This paper focuses on a forward-looking research topic that route commercial, vacant self-driving vehicles so that the values to both businesses and passengers are improved. Importance of solutions to the new problem is discussed. We also propose a novel design which simulates behaviors of ants in nature to the vehicles. The goal of the system is to obtain an overall balance between the demands of using the services from the passengers and availability of the vehicles in all service areas. The system not only uses historical data to make decisions, it also responds promptly for demands appeared dynamically

T-PickSeer: Visual Analysis of Taxi Pick-up Point Selection Behavior

Taxi drivers often take much time to navigate the streets to look for passengers, which leads to high vacancy rates and wasted resources. Empty taxi cruising remains a big concern for taxi companies. Analyzing the pick-up point selection behavior can solve this problem effectively, providing suggestions for taxi management and dispatch. Many studies have been devoted to analyzing and recommending hot-spot regions of pick-up points, which can make it easier for drivers to pick up passengers. However, the selection of pick-up points is complex and affected by multiple factors, such as convenience and traffic management. Most existing approaches cannot produce satisfactory results in real-world applications because of the changing travel demands and the lack of interpretability. In this paper, we introduce a visual analytics system, T-PickSeer, for taxi company analysts to better explore and understand the pick-up point selection behavior of passengers. We explore massive taxi GPS data and employ an overview-to-detail approach to enable effective analysis of pick-up point selection. Our system provides coordinated views to compare different regularities and characteristics in different regions. Also, our system assists in identifying potential pick-up points and checking the performance of each pick-up point. Three case studies based on a real-world dataset and interviews with experts have demonstrated the effectiveness of our system.Comment: 10 pages, 10 figures; The 10th China Visualization and Visual Analytics Conferenc