Large-scale Intelligent Transportation Systems Research Articles

Special Issue on Methods and Applications for Large-Scale Intelligent Transportation Systems [Guest Editorial

Special Issue on Methods and Applications for Large-Scale Intelligent Transportation Systems [Guest Editorial

Optimal policy for scheduling automated guided vehicles in large-scale intelligent transportation systems

Automated electric mobility technologies have been increasingly applied to large-scale intelligent transportation systems (ITSs) to enhance productivity and efficiency. Advanced technologies have reshaped the traditional transportation systems and posed numerous challenges to the deployment and management of new ITSs. A major challenge in the real-life implementation of ITSs is how to manage a large number of automated objects in a cooperative manner. In this paper, we investigate the scheduling and routing problem of automated guided vehicles (AGVs) in a complicated ITS. Cost and efficiency are identified as the two crucial performance indicators of such a novel ITS. An easy-to-implement practical decision policy and a tailored particle swarm based solution method are designed for problem solving. In addition to the theoretical contributions, this paper also conducts a case study to validate the effectiveness and applicability of the proposed methodology, thus contributing to the planning and management of large-scale transportation systems by modeling, optimizing, and validating a new ITS deployed with AGVs.

A Spatio-Temporal Structured LSTM Model for Short-Term Prediction of Origin-Destination Matrix in Rail Transit With Multisource Data

Passenger assignment of rail transit has recently attracted increasing research interest due to its potential applications in large-scale intelligent transportation systems. In the rail transit system, the foundation of passenger assignment is passengers’ origin and destination demand (OD matrix). However, due to the nature of stochastic of the short-term dynamic OD matrix, how to accurately predict the distribution of passenger travel spatio-temporally is still an open challenge. In this paper, combined multisource data with deep learning method is proposed to improve prediction of dynamic OD matrix accuracy. Firstly, multisource data such as smart card data, weather data and mobile phone data are introduced. And after quantitative analysis of the influencing factors, choosing 31 features as model inputs. Secondly, considering the superiority of Long Short-term Memory Network in time series, we improve the structure of LSTM by redesigning the hidden layer and neuron, in view of the spatio-temporal characteristics of spatio-temporal Long Short-term Memory Network (STLSTM) of rail transit passenger flow. Finally, using the Beijing subway network which had 54,056OD for verification. Extensive experiments and evaluations on a large-scale dataset well demonstrate the superiority of STLSTM over commonly used prediction models and standard LSTM model for short-term prediction of dynamic OD matrix. In addition, the application method of multisource data in OD prediction in this paper can deal with more data from other sources to further improve the information exploit effect on passenger flow law.

Contextualized Spatial–Temporal Network for Taxi Origin-Destination Demand Prediction

Taxi demand prediction has recently attracted increasing research interest due to its huge potential application in large-scale intelligent transportation systems. However, most of the previous methods only considered the taxi demand prediction in origin regions, but neglected the modeling of the specific situation of the destination passengers. We believe it is suboptimal to preallocate the taxi into each region-based solely on the taxi origin demand. In this paper, we present a challenging and worth-exploring task, called taxi origin-destination demand prediction, which aims at predicting the taxi demand between all-region pairs in a future time interval. Its main challenges come from how to effectively capture the diverse contextual information to learn the demand patterns. We address this problem with a novel contextualized spatial–temporal network (CSTN), which consists of three components for the modeling of local spatial context (LSC), temporal evolution context (TEC), and global correlation context (GCC), respectively. First, an LSC module utilizes two convolution neural networks to learn the local spatial dependencies of taxi, demand respectively, from the origin view and the destination view. Second, a TEC module incorporates the local spatial features of taxi demand and the meteorological information to a Convolutional Long Short-term Memory Network (ConvLSTM) for the analysis of taxi demand evolution. Finally, a GCC module is applied to model the correlation between all regions by computing a global correlation feature as a weighted sum of all regional features, with the weights being calculated as the similarity between the corresponding region pairs. The extensive experiments and evaluations on a large-scale dataset well demonstrate the superiority of our CSTN over other compared methods for the taxi origin-destination demand prediction.

Vehdoop: A Scalable Analytical Processing Framework for Vehicular Sensor Networks

The vehicular sensor network (VSN) technology empowers intelligent transportation systems (ITSs) to support a wide range of road safety and traffic management applications. By taking advantage of the information collection and communication capabilities offered by VSNs, information, such as speed, travel time, dash-camera video, and so on, can be gathered from sensors embedded in vehicles and then delivered to the infrastructure to support ITS applications. The explosive growth in the availability and variety of sensor instruments as well as the number of vehicles provides us with the opportunity to create large-scale ITS applications, which demand large-scale data processing. In order to support large-scale data processing, Google proposed the MapReduce framework. The MapReduce framework provides scalability in a large-scale data cluster by performing aggregate computations as close to the data source as possible. However, supporting ITS applications over VSN is not just a matter of simply applying the existing MapReduce framework to VSN due to the limited wireless bandwidth and the highly dynamic network topology. In this paper, we propose an analytical processing framework for VSNs called Vehdoop . Vehdoop utilizes the computing capability of vehicles to efficiently process sensor data in parallel across a large number of vehicles in a decentralized manner. We conducted extensive experiments using vehicle trajectories generated from Simulation of Urban MObility (SUMO) and a network simulator, NS-3, to simulate vehicle-to-vehicle and vehicle-to-infrastructure communications. The experimental results demonstrate the superiority of Vehdoop.

A Scenario-Reconfigurable Simulator for Verifying Service-Oriented Cooperation Mechanisms and Policies of Connected Intelligent Vehicles

With the emerging vehicular network and the possible diverse applications, intelligent transportation systems (ITS) have been evolving to Cooperative ITS (C-ITS) with connected intelligent vehicles, and the topics in this field have raised more and more research interests recently. However, subjecting to the immaturity of V2X communication technology, the difficulty and high cost to deploy such large scale ITS with intelligent vehicles, emerging studies are stuck with the verification of these big C-ITS. As more and more expected, intelligent vehicles will play important roles in the future smart cities and societies, as diverse mobility carriers. Focusing on new features of these carriers, mainly covering cyber-physical fusion, vehicular networking, service-carrier and so on, one new ITS simulator QoS-CITS for such service-oriented C-ITS is designed and developed. To enhance the adaptability, a scenario reconfigurable architecture is firstly designed, in which scenes can be described via XML file. On this basis, the authors have implemented all reservation-based models of traffic objects, state-driven behaviors, cooperation mechanisms, and policies, which are proposed for service-oriented C-ITS. Through a series of experiments are conducted with different parameters and typical scenes, all simulation functions are efficiently verified. And finally, some important conclusions drawn from large amount of experiments via QoS-CITS are exhibited. It's important to note that, researchers can conduct various experiments, both the traditional Passing-Through-Intersection (PTI) problem and service-oriented cooperation, via setting parameters of QoS-CITS according to their requirements, and can also analyze the performance with statistics data recorded automatically.

Taxi Dispatch With Real-Time Sensing Data in Metropolitan Areas: A Receding Horizon Control Approach

Traditional taxi systems in metropolitan areas often suffer from inefficiencies due to uncoordinated actions as system capacity and customer demand change. With the pervasive deployment of networked sensors in modern vehicles, large amounts of information regarding customer demand and system status can be collected in real time. This information provides opportunities to perform various types of control and coordination for large-scale intelligent transportation systems. In this paper, we present a receding horizon control (RHC) framework to dispatch taxis, which incorporates highly spatiotemporally correlated demand/supply models and real-time Global Positioning System (GPS) location and occupancy information. The objectives include matching spatiotemporal ratio between demand and supply for service quality with minimum current and anticipated future taxi idle driving distance. Extensive trace-driven analysis with a data set containing taxi operational records in San Francisco, CA, USA, shows that our solution reduces the average total idle distance by 52%, and reduces the supply demand ratio error across the city during one experimental time slot by 45%. Moreover, our RHC framework is compatible with a wide variety of predictive models and optimization problem formulations. This compatibility property allows us to solve robust optimization problems with corresponding demand uncertainty models that provide disruptive event information.

A Large-Scale SUMO-Based Emulation Platform

A hardware-in-the-loop simulation platform for emulating large-scale intelligent transportation systems is presented. The platform embeds a real vehicle into SUMO, a microscopic road traffic simulation package. Emulations, consisting of the real vehicle, and potentially thousands of simulated vehicles, are run in real time. The platform provides an opportunity for real drivers to gain a feel of being in a large-scale connected vehicle scenario. Various applications of the platform are presented.

A Proactive Parallel Complex Event Processing Method for Large-Scale Intelligent Transportation Systems

Intelligent Transportation Systems (ITS) is one of the important application areas of the Internet of Things (IoT). The key issue is how to process the huge events generated by IoT system to support ITS. In this paper a proactive parallel complex event processing method is proposed for congestion control in large-scale ITS. A Bayesian model averaging method is used to obtain accurate predictions under different event context. Based on the predictive analysis, a parallel Markov decision processes model is designed to support decision making for large-scale ITS. An optimized parallel policy iteration algorithm is proposed based on state partition and policy decomposition. The experimental evaluations show that this method has good accuracy and scalability when used to process congestion control in large-scale ITS.

Developing an Evaluation Approach to Assess Large-Scale Improvements in Intelligent Transportation System Infrastructure

Intelligent transportation systems (ITS) can include multiple technologies and applications to improve the efficiency and effectiveness of a transportation system or network. These applications are deployed with the hope of achieving the goals and objectives of multiple stakeholders. This research includes a methodology to evaluate large-scale ITS infrastructure projects using the I-91 ITS project as a case study. The methodology includes a literature review; a clear definition of project goals, objectives, and intended outcomes; development of hypotheses for project outcomes; specific measures of effectiveness; data-collection methods; and criteria to measure the success of achieving the objective. The I-91 ITS project team should consider the following recommendations as next steps in conducting an ITS evaluation: identify and prioritize the goals and objectives, develop a multiphase evaluation approach, identify existing sources of predeployment data, identify missing data requirements, and document the existing communication protocol before deployment. Such a large-scale evaluation requires extensive effort, and priority should be given to developing a multiphase approach. This research may be used to develop an evaluation plan, which is recommended as a component of the six-step process outlined in the evaluation guidelines from the U.S. Department of Transportation.

Common Data Format Archiving of Large-Scale Intelligent Transportation Systems Data for Efficient Storage, Retrieval, and Portability

Intelligent transportation system (ITS) sensor networks, such as road weather information and traffic sensor networks, typically generate enormous amounts of data. As a result, archiving, retrieval, and exchange of ITS sensor data for planning and performance analysis are becoming increasingly difficult. An efficient ITS archiving system that is compact and exchangeable and allows efficient and fast retrieval of large amounts of data is essential. A proposal is made for a system that can meet the present and future archiving needs of large-scale ITS data. This system is referred to as common data format (CDF) and was developed by the National Space Science Data Center for archiving, exchange, and management of large-scale scientific array data. CDF is an open system that is free and portable and includes self-describing data abstraction. Archiving traffic data by using CDF is demonstrated, and its archival and retrieval performance is presented for the Minnesota Department of Transportation–s 30-s traffic data collected from about 4,000 loop detectors around Twin Cities freeways. For comparison of the archiving performance, the same data were archived by using a commercially available relational database, which was evaluated for its archival and retrieval performance. This result is presented, along with reasons that CDF is a good fit for large-scale ITS data archiving, retrieval, and exchange of data.