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Online ride-hailing as an innovative travel mode becomes increasingly popular in cities around the world. To improve the efficiency of dispatching system of ride-hailing, many ride-hailing demand prediction models based on deep learning architecture have been proposed to reduce the gross travel distance between drivers and passengers. However, in most of these prediction models, only the error metrics are used for performance evaluation. There is scarce evidence on how much the demand prediction model reduces the unnecessary travel distance in reality. In this study, a multi-scenario-based method is proposed to evaluate technical potentials of the ride-hailing demand prediction model in ride-hailing dispatching system simulation. The ride-hailing dispatching is simulated in three scenarios: traditional dispatching system, prediction model-based dispatching system, and perfect prediction model-based dispatching system. One-month data of Didi Express service provided by Didi Chuxing GAIA Initiative in Chengdu is employed to support the simulation. Two terms, empty distance and relative performance, are introduced as the criteria of prediction model performance measurement. Simulation results reveal that the total empty distance reduced 1,164 km per day by using the prediction model compared with the traditional dispatching system. The relative performance is only 58.9% compared with the perfect prediction model-based dispatching system. [ABSTRACT FROM AUTHOR]

The on-road braking emission has been proved by former studies to account for a considerable part of on-road transportation. To improve cleaner air in urban area, the spatio-temporal analysis on emission performance of on-road braking is necessary as a guideline for decision-making. In this paper, we propose a framework for analysis on the particle matter emission of on-road vehicle braking events on an urban scale. We used massive vehicle trajectories in Tokyo area with short time interval as the database for analysis. From the result, we found that within the in the study area, the average driving distance during braking takes up about 20.60% of total driving distance. The average quantity of PM10 emission from braking for each driving trajectory is 14.09 mg and the one from exhaust emission is 35.36 mg. The emission from braking can averagely occupy 39.85% of exhaust emission. What's more, in our finding, the braking emission from heavy duty vehicle is 2.33 times of light duty vehicle. From the spatial distribution of PM10 braking emission, we found that braking emission usually happened in the city center and popular crowded areas due to the large traffic volume, as well as the main trunk roads of capital expressway or highway. We also found a different spatial pattern between the light duty vehicle and heavy-duty vehicle. In temporal change, we found that rapid peaks during the rush hour on weekday and contrastive stabilization on weekend. We believe our finding can provide a clearer pattern and understanding on the emission behavior of on-road vehicle braking. Image 1 • Proposing a framework to for analysis on the particle matter emission of on-road vehicle braking events on an urban scale. • Proposing a method to detect and mine emission from braking behaviors from GPS trajectory data of vehicle travelling. • Over twenty million historical GPS points of individual vehicle in Tokyo area are employed for analysis. • We found that emission from braking can occupy 39.85% of exhaust emission and HDV emits 2.33 times of LDV. [ABSTRACT FROM AUTHOR]

In recent years, Internet-supported non-dock bicycle-sharing service had emerged and gradually become warmly applauded by the public. In the non-dock bicycle-sharing system planning, the definition of coverage area and the division of sub service areas are highly essential and fundamental, which get little attention in the existing studies. In this paper, we propose a market-oriented methodology for non-dock bicycle-sharing system planning. Firstly, the potential demand of bicycle-sharing use is identified using mobile phone data. Then, a link network is constructed to represent the spatial distribution of potential demand. The algorithm of community detection is applied to the link network to discover the densely connected nodes in the area and define the division of sub service areas. Finally, the potential patterns of bicycle-sharing use, the spatial distribution of the potential demand, and the potential emission reduction are analysed based on the resulting division of sub service areas. The proposed method is tested using a data set of approximately 34 million GPS trajectories obtained in Tokyo. The area of Tokyo is subdivided into 21 sub service areas. Suggestions for bicycle management, infrastructure development, and the bicycle-sharing system planning are given regarding sub service areas with different properties. The potential emission reduction for the introduction of bicycle-sharing system is calculated in the area of Tokyo. Image 1 • A data set of 34 million trajectories is adopted to develop an integrated method. • A method of sub-area division for the bicycle-sharing system is proposed. • Travel pattern of potential bicycle-sharing behavior is described and analyzed. • Potential emission reduction of promoting bicycle-sharing is analyzed. [ABSTRACT FROM AUTHOR]