The vehicle routing problem

The growing size of cities and increasing population mobility have determined a rapid increase in the number of vehicles on the roads, which has resulted in many challenges for road traffic management authorities in relation to traffic congestion, accidents, and air pollution. Over the recent years, researchers from both industry and academia have been focusing their efforts on exploiting the advances in sensing, communication, and dynamic adaptive technologies to make the existing road traffic management systems (TMSs) more efficient to cope with the aforementioned issues in future smart cities. However, these efforts are still insufficient to build a reliable and secure TMS that can handle the foreseeable rise of population and vehicles in smart cities. In this survey, we present an up-to-date review of the different technologies used in the different phases involved in a TMS and discuss the potential use of smart cars and social media to enable fast and more accurate traffic congestion detection and mitigation. We also provide a thorough study of the security threats that may jeopardize the efficiency of the TMS and endanger drivers' lives. Furthermore, the most significant and recent European and worldwide projects dealing with traffic congestion issues are briefly discussed to highlight their contribution to the advancement of smart transportation. Finally, we discuss some open challenges and present our own vision to develop robust TMSs for future smart cities.

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A Communications-Oriented Perspective on Traffic Management Systems for Smart Cities: Challenges and Innovative Approaches

Due to the increase in vehicle transit and congestion in road networks, providing information about the state of the traffic to commuters has become a critical issue for Advanced Traveller Information Systems. These systems should assist users in making pre-trip and en-route decisions and, for this purpose, delivering travel time information is very useful because it is very intuitive and easily understood by all travellers. The aim of this paper is to present a global view of the literature on the modelling of travel time, introducing essential concepts and giving a thorough classification of the existing techniques. Most of the attention will focus on travel time estimation and travel time prediction, which are two of the most relevant challenges in travel time modelling. The definition and goals of these two modelling tasks along with the methodologies used to carry them out will be further explored and categorised.

A review of travel time estimation and forecasting for Advanced Traveller Information Systems

A Taxonomic Review of Metaheuristic Algorithms for Solving the Vehicle Routing Problem and Its Variants

Lately, the Vehicle Routing Problem (VRP) in the city, known as City VRP, has gained popularity with its importance in city logistics. Similar to city logistics, City VRP mainly differs from conventional VRP in terms of the stakeholders involved, namely the shipper, carrier, resident, and administrator. Accordingly, this paper surveys the City VRP literature categorized by stakeholders and summarizes the constraints, models, and solution methods for VRP in urban cities. City VRPs are also analyzed based on the problem of interest considered by the stakeholders and the corresponding models that have been proposed in response. Through this review, we identify the state of the art of City VRP, highlight the core challenging issues, and suggest some potential research area in this field that have remained underexplored.

City Vehicle Routing Problem (City VRP): A Review

The dynamic vehicle routing problems (DVRP) is an extension of vehicle routing problems (VRP) in order to consider possible variations of travel times in the network. In this research, a two-stage framework for solving dynamic vehicle routing problem is proposed. In the first stage, the sweep method is adopted in vehicle assignment. In the second stage, a tabu search algorithm is implemented to improve routes under real-time information. The framework is implemented in an object-oriented approach and possible benefit from real-time information is illustrated through numerical simulation. The simulation-assignment model, DynaTAIWAN is applied in numerical simulation to evaluate real-time routing strategies in a traffic network. Numerical experiments are conducted in a 50 Nodes Network and a Taichung City. The results show that positive benefits could be achieved through utilization of real-time information with careful design.

An object-oriented evaluation framework for dynamic vehicle routing problems under real-time information

Travel time prediction in Advanced Traveler Information Systems is an important issue, because travel time is a major factor in motorists' decisions to avoid congestion and incidents. A simulation-assignment-based travel time prediction model for traffic corridors is constructed in this paper. Based on the concept of simulation-assignment models, two algorithms-the flowand the vehicle-based models-are proposed for travel time prediction. One of the critical issues in simulation-assignment models is how reliable time-dependent origin-destination (O-D) trip tables are generated. A dynamic O-D estimation and prediction procedure is developed to generate time-dependent O-D demand data for simulating vehicle movements using DynaTAIWAN: a simulation-assignment model. The empirical travel time data that were collected from electronic toll stations are used to validate the travel times that were predicted by the proposed models. The mean absolute percentage errors (RMSPEs) and root-mean-square percentage errors are less than 20% and 26% for the vehicle-based model and less than 10% and 12% for the flow-based model, respectively. The results show that the proposed algorithms predict reasonable travel times for traffic corridors.

Simulation-Assignment-Based Travel Time Prediction Model for Traffic Corridors

This research aims at developing an integrated dynamic simulation-assignment model, DynaTAIWAN under mixed traffic flow conditions, for Advanced Traffic Management Systems as well as Advanced Traveler Information Systems. The model is composed of two layers, namely simulation-layer and real-time control layer. The simulation layer is designed to simulate traffic flow patterns according to assumed tripmaker characteristics and/or under a set of given conditions; the real-time control layer receives real-time vehicle information and forecast short-term traffic flow patterns. In this paper, the simulation layer is discussed in detail and numerical experiments are conducted to illustrate functional capabilities of the proposed model. In the simulation process, each vehicle is moved and tracked individually. Four different vehicle types are explicitly considered in DynaTAIWAN, including car, bus, motorcycle, and truck. Vehicles are moving along the link through macroscopic flow relationships, speeds of each type of vehicle are adjusted. Numerical experiments are conducted in a 50-node test network and a Taichung City Network.

Dynamic Simulation-Assignment Model (DynaTAIWAN) under Mixed Traffic Flows for ITS Applications

Traffic assignment distributes Origin-Destination (OD) trips in a network, and determines the flow patterns in a traffic network. This research aims at developing simulation-based algorithm for dynamic traffic assignment problems under mixed traffic flow considerations. Four different physical vehicle types are explicitly considered and modeled, including car, bus, motorcycle, and truck. Four different behavioral rules, pre-specified-path driver, user-equilibrium driver, system-optimization driver, and real-time information driver, are considered in the solution procedure. The DTA algorithm consists of an inner loop that incorporates a direction finding mechanism for the search process for System Optimization (SO) and User Equilibrium (UE) classes based on the simulation results of the current iteration, including experienced vehicular trip times and marginal trip times. In order to understand tripmaker acceptance toward route guidance, a survey is conducted to explore possible behavioral classifications and associated percentages. Numerical experiments are conducted in a test network to illustrate the capabilities of the simulation-based DTA procedures, and to observe how system performs under multiple user classes conditions, including multiple user behavior rules and multiple physical vehicle classes.

Simulation-Based Dynamic Traffic Assignment Model for Mixed Traffic Flows

With the advancement of intelligent transportation systems (ITS), the current and future emergency medical services (EMS) systems are characterized by real-time response, specialization, and decentralization. In order to provide real-time response of medical services, several components are essential, such as dispatching, routing, emergency facilities, and patients medical history. This paper proposes a CORBA (common object request broker architecture)-based GIS-T (geographic information systems for transportation) framework for the dispatching and assignment of medical vehicles for emergency services via wireless communication. The framework combines distributed object computing, GIS-T, and transportation models in a single system, thus provides flexibility and interoperability among existing medical service systems. The framework utilizes the concept of distributed systems and capabilities of CORBA to design objects and dynamically updated medical facilities information in order to provide ambulances with up-to-date information. A real roadway network is used in the numerical experiments to illustrate dynamic aspects of the proposed system.

Tsai-Yun Liao

Papers

An object-oriented evaluation framework for dynamic vehicle routing problems under real-time information

Simulation-Assignment-Based Travel Time Prediction Model for Traffic Corridors

Dynamic Simulation-Assignment Model (DynaTAIWAN) under Mixed Traffic Flows for ITS Applications

Simulation-Based Dynamic Traffic Assignment Model for Mixed Traffic Flows

A CORBA-based GIS-T for ambulance assignment