Search

Ride-sharing has transformed people's travel habits with the development of various ride-sharing platforms, which can enhance the utilization of transportation resources, alleviate traffic congestion, and reduce carbon emissions. However, the development of a general and efficient matching framework is challenging due to the dynamic real-time conditions and uncertainty of ride-sharing problems in the real world. Additionally, previous research has identified limitations in terms of model practicability and algorithmic solution speed. To address these issues, a two-stage dispatching approach for one-to-many ride-sharing with sliding time windows is proposed. The dynamic ride-sharing problem is formally defined, and an integer programming model is constructed to solve it. A multi-rider distance and time constraint algorithm uses a distance matrix and sliding time windows to preprocess data before matching is proposed, thereby optimizing data quality and improving computational efficiency. The ride-sharing process is divided into a reservation order matching stage based on path similarity and a real-time order matching stage based on path distance degree. A two-stage collaborative mechanism is designed to guide the collaboration of the two stages. Furthermore, numerical experiments are conducted using two real-world datasets from developing and developed country regions to verify the efficiency and practicability of the proposed approach. [ABSTRACT FROM AUTHOR]

The road network maintaining stability is critical for guaranteeing urban traffic function. Therefore, the vulnerable links need to be identified accurately. Previous vulnerability research under static condition compared the operating states of the old equilibrium before the event and the new equilibrium after the event to assess vulnerability ignoring the dynamic variation process. Does road network vulnerability change over time? This paper combines the vulnerability assessment with the traffic flow evolution process, exploring the road network vulnerability evaluation from the perspective of time dimension. More accurate identification and evaluation of vulnerable nodes and links can help to strengthen the ability of road network resisting disturbances. A modified dynamic traffic assignment (DTA) model is established for dynamic path selection (reselect the shortest path at the end of each link) based on the dynamic user optimal (DUO) principle. A modified cell transmission model is established to simulate the traffic flow evolution processes. The cumulative and time-varying index of vulnerability assessment is established from the viewpoint of traveler's time loss. Then the road network vulnerability assessment combined the traffic flow model with the vulnerability index. The road network vulnerability assessment of Bao'an Central District of Shenzhen, China, reveals that road network vulnerability does contain a dynamic process, and vulnerable links in each phase can be exactly identified by the model. Results showed that the road network would have a large vulnerability during the disordered phase when the main road fails. Therefore, prioritizing the smooth flow of main roads can weaken the impact of road network vulnerability exposure. [ABSTRACT FROM AUTHOR]

This study is devoted to handle disturbances occurring in a subway network during the off-peak period. By dissecting the relationship of transfer connections between connected lines, transfer passengers and non-transfer passengers as well as operating agencies are taken into consideration, respectively, and a trade-off method for timetable rescheduling is proposed to minimize the waiting time of all transfer passengers as well as the deviation from the planned timetable. We adopt the 𝜀 -constraint method to convert the bi-objective model to an integer linear programing model with single objective, which can be solved by CPLEX within seconds. The method is validated by some numerical experiments based on a real-world case derived from the Beijing Subway network. Results show that the proposed method is able to generate approximate Pareto optimal solutions and to provide decision support for dispatchers. [ABSTRACT FROM AUTHOR]

• Context-specific built environment and transit travel behaviour of urban villages are measured in each village unit. • Total, direct and indirect effects of built environment on transit travel are revealed. • Residents in urban villages use less public transit when residential density increases. • Mixed land use has no significant effect on transit travel behavior. Context-specific research are necessary to promote public transit by optimizing the built environment in the process of urban renewal. Using data of residential travel survey in Shenzhen, China in 2014, this paper investigated how built environment affected transit travel in urban villages where low-income group resided, and they are all tenants. Structural equations modelling was employed to express the effects of socio-economic, public transit service related and built environment variables on travel mode choice, travel time and distance, with control for residential self-selection. It is found that after considering the self-selection effect, the influence of density and transit availability on travel distance, travel time and transit mode choice in urban villages are still significant. For urban villages, transit availability matters greatest for transit promotion, but the negative effect of density on transit mode choice and none effect of mixed land use are unusual as previous findings. This is partly because of relatively low income, low car ownership, and less elastic travel demand of tenants living in urban villages. Our findings provide some insights into transit-oriented urban renewal that when transforming urban villages, emphasis should be put on enhancement of transit availability, and the mixed land use could be put in the last consideration when there is limited time and funds. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the implementation flexibility of multiperiod rail line design in a linear monocentric city. Three alternatives (fast-tracking, deferring, and do-nothing-alternative (DNA) of a candidate rail line project) are examined, based on an in-depth uncertainties analysis of the demand side for this candidate rail line project. Conditions for the three alternatives of fast-tracking, deferring, and DNA are analytically explored and an illustrative example is given to demonstrate the application of the proposed models. Insightful findings are reported on the interrelationship between the rail line length and spatial and temporal correlation of population distribution as well as the implication of the correlation in practice. Sensitivity analyses are carried out in several scenarios in another numerical example to show the proposed conditions of three alternatives. [ABSTRACT FROM AUTHOR]

In contrast to private cars, rail transit systems are a more effective way to deal with the emerging challenges in cities with high population densities, such as congestion, air pollution, and traffic emissions. Rail transit systems, however, are commonly costly, due to substantial investments in construction and maintenance. It is thus necessary to design the candidate rail transit systems carefully to ensure public transport accessibility and sustainability, with consideration of the space-time correlation of population densities. In this paper, the space-time correlations of population densities are incorporated into the design of a candidate rail transit line over years. A closed-formed mathematical programming model is proposed, with an optimisation objective of social welfare budget maximisation. The social welfare budget is defined as the summation of the expected social welfare and social welfare margins. The model decision variables include rail line length, rail station number, and project start time of the candidate rail transit line. The analytical solutions for the proposed rail design model are given explicitly for different scenarios with various constraints. [ABSTRACT FROM AUTHOR]

Aiming at the dense urban road network vulnerability without structural negative consequences, this paper proposes a novel non-structural road network vulnerability analysis framework. Three aspects of the framework are mainly described: (i) the rationality of non-structural road network vulnerability, (ii) the metrics for negative consequences accounting for variant road conditions, and (iii) the introduction of a new vulnerability index based on user exposure. Based on the proposed methodology, a case study in the Sioux Falls network which was usually threatened by regular heavy snow during wintertime is detailedly discussed. The vulnerability ranking of links of Sioux Falls network with respect to heavy snow scenario is identified. As a result of non-structural consequences accompanied by conceivable degeneration of network, there are significant increases in generalized travel time costs which are measurements for “emotionally hurt” of topological road network. [ABSTRACT FROM AUTHOR]

In this paper, we propose a face recognition algorithm by incorporating a neighbor matrix into the objective function of sparse coding. We first calculate the neighbor matrix between the test sample and each training sample by using the revised reconstruction error of each class. Specifically, the revised reconstruction error ( RRE ) of each class is the division of the l 2 -norm of reconstruction error to the l 2 -norm of reconstruction coefficients, which can be used to increase the discrimination information for classification. Then we use the neighbor matrix and all the training samples to linearly represent the test sample. Thus, our algorithm can preserve locality and similarity information of sparse coding. The experimental results show that our algorithm achieves better performance than four previous algorithms on three face databases. [ABSTRACT FROM AUTHOR]

Linear regression uses the least square algorithm to solve the solution of linear regression equation. Linear regression classification (LRC) shows good classification performance on face image data. However, when the axes of linear regression of class-specific samples have intersections, LRC could not well classify the samples that distribute around intersections. Moreover, the LRC could not perform well at the situation of severe lighting variations. This paper proposes a new classification method, kernel linear regression classification (KLRC), based on LRC and the kernel trick. KLRC is a nonlinear extension of LRC and can offset the drawback of LRC. KLRC implicitly maps the data into a high-dimensional kernel space by using the nonlinear mapping determined by a kernel function. Through this mapping, KLRC is able to make the data more linearly separable and can perform well for face recognition with varying lighting. For comparison, we conduct on three standard databases under some evaluation protocols. The proposed methodology not only outperforms LRC but also takes the better performance than typical kernel methods such as kernel linear discriminant analysis and kernel principal component analysis. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a two-phase face recognition method in frequency domain using discrete cosine transform (DCT) and discrete Fourier transform (DFT). The absolute values of DCT coefficients or DFT amplitude spectra are used to represent the face image, i.e. the transformed image. Then a two-phase face classification method is applied to the transformed images. This method is as follows: its first phase uses the Euclidean distance formula to calculate the distance between a test sample and each sample in the training sets, and then exploits the Euclidean distance of each training sample to determine K nearest neighbors for the test sample. Its second phase represents the test sample as a linear combination of the determined K nearest neighbors and uses the representation result to perform classification. In addition, we use various numbers of DCT coefficients and DFT amplitude spectra to test the effect on our algorithms. The experimental results show that our method outperforms the two-phase face recognition method based on space domain of face images. [Copyright &y& Elsevier]

Last mile transportation is important in both freight and passenger transport as it accounts for a large portion of the costs and emissions in the transportation industry. In urban transport, the continuously growing travel demands and the rapid development of mass transit systems place a high stress on last mile transportation, which is a vital but underdeveloped part of urban transportation systems. This underdevelopment greatly impedes the further improvement of bus sharing rates and the realisation of sustainable transportation. Therefore, this research proposes a data-driven method to design shuttle services to improve the efficiency and convenience of last mile transportation. Specifically, a unified tool is developed to identify the last mile travel demands from various data sources. Based on these demands, the locations of bus stop are planned through an improved clustering algorithm, and the routing and scheduling of shuttle services are designed using a data-driven method. In addition, a simulation-based cost-benefit analysis is conducted to evaluate the performances of shuttle services in different areas. Finally, a case study using bicycle-sharing data in Shanghai is presented to demonstrate the working process of the proposed method and verify its performance. [ABSTRACT FROM AUTHOR]

Bus travel time often fluctuates greatly due to disturbance factors such as traffic conditions and passenger flow, which causes a reduction in the transit service as it cannot be provided as planned. Bus dispatching is necessary to handle this problem. Two crucial factors for real-time bus dispatching have been analysed in this study namely the bus arrival time prediction and timetable redesign. First, a bus arrival time prediction model combining Support Vector Regression (SVR) and Kalman Filter (K-SVR) was proposed. The headway is selected as an intermediate variable to improve the prediction accuracy by limiting the disturbance factors between two adjacent bus stops. Besides, by adjusting the parameters of the Kalman Filter, the predictive results are more suitable for practice to avoid the frequency of unnecessary timetable adjustment. Furthermore, an automatic timetable redesign method is given based on the proposed circle search algorithm, which can minimise the impact on the initial schedule by finding the minimum adjustment range in consideration of the normal working order. A case study in Shenzhen, China was conducted and the results verify that the K-SVR method could improve the prediction accuracy especially in peak hours. On this basis, in comparison with the traditional method, the timetable redesign algorithm could allocate the transit resources appropriately and perform well on indicators such as the headway fluctuation, compliance rate of headways, and schedule completeness. Moreover, it is more operable and valuable as a result of sending the departure instructions appropriately and maintaining the normal working order of drivers realistically. [ABSTRACT FROM AUTHOR]

The cost-efficiency of public transport systems is a helpful measure for regulators to establish an effective subsidy mechanism. This study presents a cost-efficiency measurement model of bus routes based on stochastic frontier analysis, taking into account not only heterogeneous outputs but also operating environment factors. In addition, four new operating environment factors that can affect the efficiency of bus routes are introduced. An empirical analysis, based on 39 routes operating in Jiangyin City, China, is presented. It was found that vehicle kilometres have a bigger impact on total operating costs than other parameters such as fixed inputs and fuel price. Operating environment factors such as average bus stop density and the percentage of bus lanes have significant influences on cost-inefficiency differentials. Furthermore, the measurement of cost-efficiency enables identification of the difference between actual total operating costs and efficient total operating costs at a bus route level. The proposed approach could support regulators in setting reasonable cost benchmarks based on efficient total operating costs. [ABSTRACT FROM AUTHOR]