Search Results (6)

In the context of global suburbanization and population aging, a low-speed, automated vehicle (LSAV) system provides essential mobility services in suburban residential areas. Although extensive studies on shared autonomous vehicle (SAV) services have been conducted, quantitative investigations on the operation of suburban LSAV systems are limited. Based on a demonstration pilot project of an autonomous vehicle called “Slocal Automated Driving”, we investigated the performance of an SAV system considering several scenarios in Kozoji Newtown, a suburban commuter town in Japan. The agent-based simulation results revealed that 40 LSAVs can satisfy the demands of 2263 daily trips with an average wait time of 15 min. However, in the case of a high-speed scenario, the same fleet size improved the level of service (LOS) by reducing the average wait time to two and a half minutes and halving the in-vehicle time. By contrast, the wait time in terms of the average and 95th percentile of the no-sharing ride scenario drastically deteriorated to an unacceptable level. Based on the fluctuations of hourly share rates, wait times, and the number of vacant vehicles, we determined that preparing for the potential fleet insufficiency periods from 7:00–13:00 and 15:00–18:00 can improve the LOS. Full article

With increasing availability of alternative mobility options for city transportation system, it is necessary to better understand how emerging mobility options are impacting the travel demand and consumer-social surplus. However, few study have been conducted to evaluate the social welfare effects of the range of vacant trips in ride-sourcing service modes. This paper identified the vacant trip and loading rate evaluation model under the ride-sourcing service mode to enhance the effective operation of the different mobility services under numerical illustrations. The solution can also offer some beneficial guidance and theoretical basis for ride-sourcing systems in regard to planning and management aspects. Full article

In China, the traditional taxi industry is conforming to the trend of the times, with taxi drivers working with e-hailing applications. This reform is of great significance, not only for the taxi industry, but also for the transportation industry, cities, and society as a whole. Our goal was to analyze the changes in driving behavior since taxi drivers joined e-hailing platforms. Therefore, this paper mined taxi trajectory data from Shanghai and compared the data of May 2015 with those of May 2017 to represent the before-app stage and the full-use stage, respectively. By extracting two-trip events (i.e., vacant trip and occupied trip) and two-spot events (i.e., pick-up spot and drop-off spot), taxi driving behavior changes were analyzed temporally, spatially, and efficiently. The results reveal that e-hailing applications mine more long-distance rides and new pick-up locations for drivers. Moreover, driver initiative have increased at night since using e-hailing applications. Furthermore, mobile payment facilities save time that would otherwise be taken sorting out change. Although e-hailing apps can help citizens get taxis faster, from the driver’s perspective, the apps do not reduce their cruising time. In general, e-hailing software reduces the unoccupied ratio of taxis and improves the operating ratio. Ultimately, new driving behaviors can increase the driver’s revenue. This work is meaningful for the formulation of reasonable traffic laws and for urban traffic decision-making. Full article

The purpose of this study is to enhance the performance of Multistream Fast Transmission Control Protocol (TCP) keeping in view the recent web-based applications that are being deployed on long-range, high-speed, and high-bandwidth networks. To achieve the objective of the research study, a congestion control after fast-recovery module for congestion control scheme of Multistream Fast TCP is proposed. The module optimized the performance of the protocol by reducing the time that is required to consume the available bandwidth after a fast-recovery phase. The module is designed after studying additive-increase, multiplicative-decrease and rate-based congestion window management schemes of related transport protocols. The module adjusts the congestion window on receipt of each individual acknowledgment instead of each round trip time after the fast-recovery phase until it consumes vacant bandwidth of the network link. The module is implemented by using Network Simulator 2. Convergence time, throughput, fairness index, and goodput are the parameters used to assess the performance of proposed module. The results indicate that Enhanced Multistream Fast TCP with congestion control after fast recovery recovers its congestion window in a shorter time period as compared to multistream Fast TCP, Fast TCP, TCP New Reno, and Stream Control Transmission Protocol. Consequently, Enhanced Multistream Fast TCP consumes the available network bandwidth in lesser time and increases the throughput and goodput. The proposed module enhanced the performance of the transport layer protocol. Our findings demonstrate the performance impact in the form of a decrease in the convergence time to consume the available network bandwidth and the increase in the throughput and the goodput. Full article

A popular phenomenon in the street-hailing taxi system is the imbalanced mobility services between city central and outside downtown areas, which leads to unmet demand outside downtown areas and competitions in city central areas. Understanding taxi drivers’ customer-searching behaviors is crucial to addressing the phenomenon and redistributing the taxi supply. However, the current literature ignores or simply models the taxi drivers’ behaviors, in particular, lacks the in-depth discussions on individuals’ heterogeneity. This study introduces the latent class model to identify the internal and external factors influencing the taxi drivers’ destination choice after the last drop-offs. Beyond the influencing factors, the modeling structure captures the heterogeneity in vacant taxicab drivers through introducing latent classes. The proposed model outperforms other discrete choice models, for instance, multinomial logit, nested logit, and mixed logit, based on the two study cases developed from the New York City yellow taxicab system. The empirical results first statistically indicate the existence of latent classes, which further empirically prove the heterogeneity in the choices by vacant taxicab drivers while searching customers. Moreover, we obtain a set of internal and external factors influencing the customer searching behaviors. For example, the taxicab drivers are sensitive to the demand at the search destination areas and the distance from the last drop-off location to the search destination areas and behave identically in particular under the conditions of high demand and short search distance. On the other hand, the external variables have different impacts on customer searching behaviors across the different groups of drivers in the both study cases, including peak hours, weekday, holiday, earned fare from last occupied trip, raining hours, and flight arrivals at airports. In final, the proposed modeling structure and findings are useful as a sub-model of taxi system modeling while developing strategies, as well as as a regional planning tool for taxi supply estimations. Full article

The unbalanced distribution of taxi passengers in space and time affects taxi driver performance. Existing research has studied taxi driver performance by analyzing taxi driver strategies when the taxi is occupied. However, searching for passengers when vacant is costly for drivers, and it limits operational efficiency and income. Few researchers have taken the costs during vacant status into consideration when evaluating taxi driver performance. In this paper, we quantify taxi driver performance using the taxi’s average efficiency. We propose the concept of a high-efficiency single taxi trip and then develop a quantification and evaluation model for taxi driver performance based on single trip efficiency. In a case study, we first divide taxi drivers into top drivers and ordinary drivers, according to their performance as calculated from their GPS traces over a week, and analyze the space-time distribution and operating patterns of the top drivers. Then, we compare the space-time distribution of top drivers to ordinary drivers. The results show that top drivers usually operate far away from downtown areas, and the distribution of top driver operations is highly correlated with traffic conditions. We compare the proposed performance-based method with three other approaches to taxi operation evaluation. The results demonstrate the accuracy and feasibility of the proposed method in evaluating taxi driver performance and ranking taxi drivers. This paper could provide empirical insights for improving taxi driver performance. Full article