Showing papers by "Xiaolei Ma published in 2018"

Two-echelon location-routing optimization with time windows based on customer clustering

Abstract: This paper develops a three-step customer clustering based approach to solve two-echelon location routing problems with time windows. A bi-objective model minimizing costs and maximizing customer satisfaction is formulated along with an innovative measurement function to rank optimal solutions. The proposed methodology is a knowledge-based approach which considers customers locations and purchase behaviors, discovers similar characteristics among them through clustering, and applies exponential smoothing method to forecast periodic customers demands. We introduce a Modified Non-dominated Sorting Genetic Algorithm-II (M-NSGA-II) to simultaneously locate logistics facilities, allocate customers, and optimize the vehicle routing network. Different from many existing version of NSGA-II, our algorithm applies partial-mapped crossover as genetic operator, instead of simulated binary crossover, in order to properly handle chromosomes. The initial population is generated through a nodes’ scanning algorithm which eliminates sub-tours. Finally, to demonstrate the applicability of our mathematical model and approach, we conduct two empirical studies on generated benchmarks and the distribution network of a company in Chongqing city, China. Further comparative analyses with multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO) algorithm indicate that M-NSGA-II performs better in terms of solution quality and computation time. Results also support that: (1) the formation of clusters containing highly similar customers improves service reliability, and favors a productive customer relationship management; (2) considering product preference contributes to maximizing customer satisfaction degree and the effective control of inventories at each distribution center; (3) clustering, instead of helping to improve services, proves detrimental when too many groups are formed. Thus, decision makers need to conduct series of simulations to observe appropriate clustering scenarios.

Vehicle trajectory reconstruction from automatic license plate reader data

Abstract: Using perception data to excavate vehicle travel information has been a popular area of study In order to learn the vehicle travel characteristics in the city of Ruian, we developed a common metho

Joint Analysis of the Commuting Departure Time and Travel Mode Choice: Role of the Built Environment

Abstract: This paper aims to conduct an empirical study to evaluate the influence of built environment features and socioeconomic factors on commuters’ simultaneous choice of departure time and travel mode. Using Kunming, China, as the study region, the 2015 Regional Household Travel Survey and 2016 Point of Interest data are used in the analysis. The results show that, in addition to socioeconomic factors, built environment, such as the density of residential building, employment, and service facility are correlated with joint choice behavior. Moreover, there exist differences regarding the influence of built environment and socioeconomic factors on departure time and travel mode choice. The dissimilarity parameters show that commuters prefer to shift travel mode than departure time generally when travel condition alters. In order to examine the policy measures’ potential performance, the paper conducts simulation tests based on the Monte Carlo method. The simulation results show that congestion pricing of car travel during peak hours can reduce the number of commuting trips, and reducing travel time of public transit would be a better strategy to attract more passengers during peak hours. Moreover, reasonable land use planning, such as building more bus stops around commuters’ home location, would be a long term and fundamental approach to reduce mobile-source emissions and attract more public transit passengers.

Urban public transport passenger flow forecast method and equipment based on in-depth learning

Abstract: The invention provides an urban public transport passenger flow forecast method and equipment based on in-depth learning, used for solving the problem that an accurate method capable of forecasting future short-term passenger flow on an urban public transport line does not exist. The method disclosed by the invention comprises the following steps: establishing a grid map according to a traffic line and geographic information of stations; updating pixel values of grids corresponding to stations in the grid map according to the passenger flow information of the stations; and by taking the grid map as input of an in-depth learning algorithm, forecasting the passenger flow of the urban public transport. According to the technical scheme, the method disclosed by the invention has the advantagesthat the metro passenger flow station network is converted into picture information to serve as input of a convolutional neural network by utilizing the geographic information system, and the space information of the passenger flow is extracted. Then, the output of the convolutional neural network serves as input of a long/short-term neural network so as to extract time characteristics. Finally,spatial-temporal characteristics serve as input of a full-join neural network so as to perform passenger flow forecast.

A time-varying parameters vector auto-regression model to disentangle the time varying effects between drivers’ responses and tolling on high occupancy toll facilities

Abstract: High Occupancy Toll (HOT) lane systems are considered one of most effective countermeasures to mitigate freeway congestion. Existing studies have largely focused on developing optimal tolling strategies to maximize the benefits of congestion pricing. Limited effort has been made to model the dynamic feedback mechanism of drivers’ responses to tolling. A thorough understanding of how the interactive relationship between demands (in both HOT lane and general purpose lanes) and toll rates evolves over time is necessary. The underlying mechanism can be used directly for guiding future HOT facilities investment decisions. This study builds upon the traditional vector autoregressive model and enables its parameters to be time-varying. Such a relaxation, namely, time-varying parameter vector autoregressive model (TVP-VAR), is used to answer the following two questions: (1) Is there a time varying effect between general purpose lane volume, HOT lane volume and dynamic toll rate? (2) If there is, how to quantify such time-varying interdependencies? Based on the empirical data from loop detectors and toll logs on Washington State Route 167 (SR167), we identified the existence of time-varying effects between drivers’ responses and toll rates, and quantified the evolving interactions amongst HOT demand, general purpose demand and tolling via time-varying impulse responses. In addition, we found that drivers’ perceptions on HOT lanes across distinct geographical locations are significantly different.

Forecasting Transportation Network Speed Using Deep Capsule Networks with Nested LSTM Models

Abstract: Accurate and reliable traffic forecasting for complicated transportation networks is of vital importance to modern transportation management. The complicated spatial dependencies of roadway links and the dynamic temporal patterns of traffic states make it particularly challenging. To address these challenges, we propose a new capsule network (CapsNet) to extract the spatial features of traffic networks and utilize a nested LSTM (NLSTM) structure to capture the hierarchical temporal dependencies in traffic sequence data. A framework for network-level traffic forecasting is also proposed by sequentially connecting CapsNet and NLSTM. On the basis of literature review, our study is the first to adopt CapsNet and NLSTM in the field of traffic forecasting. An experiment on a Beijing transportation network with 278 links shows that the proposed framework with the capability of capturing complicated spatiotemporal traffic patterns outperforms multiple state-of-the-art traffic forecasting baseline models. The superiority and feasibility of CapsNet and NLSTM are also demonstrated, respectively, by visualizing and quantitatively evaluating the experimental results.

Multi-mode traffic demand influence analysis method based on spatial vector autoregression model

Abstract: The invention provides a multi-mode traffic demand influence analysis method, for different areas of public traffic demands and private cars, based on a spatial vector autoregression model. The methodmainly comprises that (1) a multi-mode traffic demand cooperation model among the areas is established, a traditional spVAR model is improved, a regional POI index is introduced to define the spatialweight for the areas of different traffic modes, and a multi-mode traffic demand spatial VAR model including the regional space structural relation is constructed; and (2) a multi-mode traffic demandcooperation strategy of different areas is provided. Pulse response and variance decomposition results of different traffic modes are solved on the basis of the constructed regional multi-mode traffic spatial VAR model, further, a spatial overflow effect of the traffic demands is obtained by analysis, and the cooperative strategy model for different spatial states and traffic states is provided and constructed. It is proved that the model can improve the availability and scientific performance of traffic efficiency.