Optimal route assignment in large scale micro-simulations

An optimal global algorithm for route guidance in advanced traveler information systems

In transportation-based cyberphysical systems (TCPS), also known as intelligent transportation systems (ITS), to increase traffic efficiency, a number of dynamic route guidance schemes have been designed to assist drivers in determining optimal routes for their travels. To determine optimal routes, it is critical to effectively predict the traffic condition of roads along the guided routes based on real-time traffic information collected by vehicular networks to mitigate traffic congestion and improve traffic efficiency. In this paper, we propose a Dynamic En-route Decision real-time Route guidance (DEDR) scheme to effectively mitigate road congestion caused by the sudden increase of vehicles and to reduce travel time and fuel consumption. DEDR considers real-time traffic information generation and transmission by vehicular networks. Based on the shared traffic information, DEDR introduces the trust probability to predict traffic conditions and to dynamically, en route, determine alternative optimal routes. DEDR also considers multiple metrics to comprehensively assess traffic conditions so that drivers can determine the optimal route with a preference to these metrics during travel. DEDR considers effects of external factors (bad weather, incidents, etc.) on traffic conditions as well. Through a combination of extensive theoretical analysis and simulation experiments, our data show that DEDR can greatly increase traffic efficiency in terms of time efficiency, balancing efficiency, and fuel efficiency, in comparison with existing schemes.

Transportation-based Cyber-Physical Systems (TCPS), also known as Intelligent Transportation Systems (ITS), have been introduced to increase traffic efficiency and safety. To reduce traffic congestion and traveling time, a number of real-time route guidance schemes have been developed to assist travelers in determining the optimal route for their transit. In this paper, we address the vulnerability issue of the route guiding process and study data integrity attacks against route guidance schemes. To be specific, we consider a generic attack, in which the adversary may compromise vehicles via wireless communication networks and then manipulate the real-time traffic information generated or forwarded by these vehicles, and finally broadcast the forged real-time traffic information into vehicular networks. We formally model the attack and quantitatively analyze its impact on the effectiveness of route guidance schemes. Our findings show that the investigated data integrity attack can effectively disrupt route guidance, resulting in significant traffic congestion, the increase of travel time, and the imbalanced use of transportation resources.

In this research process, the aim is to determine the shortest route for transporting waste with the shortest or minimum total distance. In this case, what must be considered is the transportation of waste, regarding the distance between the location of the waste source to the landfills. The problem of waste transportation routes can be made in a mathematical method. The method used in waste transportation is the Capacitated Vehicle Routing Problem (CVRP) method. In solving the problem of transporting waste using the CVRP method, distance must be minimized or optimized using the Particle Swarm Optimization Algorithm. On the initial route of work area 1 there is the o-p-q-r-s-o route which has a total distance of 133.5 Km, in which there are several villages such as the Mambang Muda TPA - Belungihit Village - Aek Hitetoras Village - Brussel Plantation Village - Aek Tapa Village - Mambang Muda TPA. And after the latest garbage transportation route uses the PSO algorithm, the optimal distance is 132.5 Km with the o-s-q-r-p-o route where the village is Mambang Muda TPA - Aek Tapa Village - Aek Hitetoras Village - Brussels Plantation Village - Belungihit Village - Mambang Muda Landfill. In WK 2 there is an initial route o-p-q-r-s-o which has a total distance of 153.9 Km, in which the route has several TPA Mambang Muda villages - Marbau Village - Lobu Rampah Village - Simpang 4 Village - Pernantian Plantation Village - Mambang Muda TPA. And after the latest garbage transportation route uses the PSO algorithm, the optimal distance and route is 127.7 Km. In work area 3 with the initial route and distance of 150 and after using the PSO algorithm, the optimal route and distance is 137.1 Km. In work area 4 with a route and distance of 127.8 Km, the optimal route and distance using the PSO algorithm is 149.8 Km. Work area 4 has a route and initial distance of 157.8 Km and the optimal PSO route and distance is 149.8. The 5th working area has an initial distance of 180.3 and the PSO route is 134.3 Km. The working area of ​​the 5 initial routes is 152.4 Km and the PSO route is 152.4.

ABSTRACTVariable message signs that provide various types of route guidance information have been widely deployed in large cities. To release proper information only using easily collected data, a simple traffic-condition-based (TCB) route guidance strategy was recently proposed. The strategy works based on the estimation of free-flow and congested traffic conditions and is capable of approximating user optimal equilibrium stably. Due to little consideration of the complexity of urban road networks, the TCB strategy is still away from field applications in urban areas. To further push the strategy toward field tests, this article improves the TCB strategy in the following aspects: supplementing the strategy with a self-regulation ability by considering existing traffic conditions; decomposing link capacity to solve the problem of overlapping routes by comparing link capacity on alternative routes; coping with stochastic traffic; and the impact of signalized intersections by utilizing aggregated data. A scenario for an urban road network in Beijing, China, is simulated to test the improved strategy, and the simulation results clearly indicate the effectiveness of the proposed improvements. The improvements extend the TCB strategy on moderately complicated urban road networks, and still have the advantages of simple diversion rules, easily obtained input data, and stable and effective diversion processes.

The passenger’s motion behavior during the check-in process will directly influence the check-in efficiency. However, little effort has been made to study this topic. This paper incorporates the passenger’s motion behavior into the route guidance strategy to enhance the check-in efficiency. Firstly, an extended social force (SF) model is developed to describe each passenger’s movement at an airport terminal. Secondly, a route guidance strategy accounting for the flight’s remaining departure time and the passenger density is designed to enhance the passengers’ check-in efficiency. Thirdly, a greedy algorithm is designed to search an optimal route for each passenger during the check-in process. Finally, the check-in process of one flight at an airport terminal is selected to testify the extended SF model and the proposed route guidance strategy. The simulation results show that (i) the extended SF model can reasonably describe the motion behavior of each passenger, and (ii) the proposed route-guidance strategy can enhance the check-in efficiency under passengers’ state described by the extend SF model. The results can guide administrators to better organize and manage the check-in process.

The integration of urban traffic signal control (TSC) system and dynamic route guidance (DRG) system became a development trend of intelligent transportation system (ITS). This paper firstly describes the characters of integration of the two systems and the theory of multi-agent system. Based on the multi-agent system theory, we present a system architecture of the integration system of TSC and DRG with two kinds interactive agent model architecture: traffic signal control agent model and dynamic route guidance agent model. We then use the TSC agent model as an example to discuss the architecture and interactive mechanism of the agent models. The multi-agent system provides a possible means for the integration of TSC and DRG. Finally this paper studies the coordination model, indicates how these agent models coordinate.

Effects of route guidance strategies on traffic emissions in urban traffic networks

Urban multi-criteria optimized route guidance by considering unspecified site satisfaction, an extended type of urban multi-objective optimized route selection, called as both NP-Hard problems and one of the branches of multi-criteria shortest path problems (MSPP). It is not only suggests a route based on route guidance principles and optimized due to routing criteria but also passes through all unspecified site(s) such as gas stations, banks determined by drivers. By proposing a novel approach on the bases of route guidance navigation system principles, virus theory (viral infection and local/site infection) and by GIS and GA utilization, this paper is come up to rate of search improvement in urban multi-criteria optimized route guidance by considering unspecified site satisfaction on real network with multiple dependent criteria. Tests of route selection for a part of north-west of Tehran traffic network are conducted and the results show the efficiency of the algorithm and support our analyses.KeywordsGISNetwork analysisMulti-criteria shortest path problemMulti objective Genetic algorithm

In order to estimate the potential benefits of route guidance and to examine the interactions between this and other traffic management studies, a simulation was performed on a representative traffic network consisting of a freeway and a parallel arterial. Different freeway and arterial incident scenarios were examined and, for each scenario, the impact of having various percentages of drivers equipped with a route guidance system was investigated. The incremental benefits of route guidance are found to be greatest for the first 20% of drivers with in-vehicle units, but further benefits continue to be obtained when the market penetration increases up to 100%. These benefits are the largest for incident scenarios, and increase with the duration of the incident. It is speculated that for normal traffic conditions only a few drivers need to reroute themselves to maintain an equilibrium assignment. However, an incident causes a greater disturbance in the equilibrium and therefore requires a greater percentage of drivers to reroute themselves before the new equilibrium can be reached.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The optimization of the travel route for a round-trip is not only a customized demand made by a large number of independently guided tourists but also an essential practical issue for the development of tourism management and tourism businesses. In light of this, the study presents a genetic algorithm (GA) as a potential solution to the problem of how to visit a number of tourist destinations within a constrained area in order to quickly determine the shortest tourist route. To traverse areas or regions with the least amount of physical exertion, select the correct and shortest route. Examining all potential routes from the starting point to the destination will allow you to determine the quickest route. A condensed explanation of the enhanced GA is provided to start. The second step is to analyze the model's construction and solution in detail. Next, an enhanced genetic algorithm (IGA) is utilized to determine the optimal travel route for visiting a variety of tourist attractions. In accordance with the optimal travel route, the required number of days and specific travel arrangements are then estimated. In conclusion, the GA is optimized, and a simulation examination of each individual's average path convergence is conducted. The results of the experiments indicate that the IGA can be effectively applied to the path planning of multiple scenic locations, the selection of the shortest travel route, the reduction of travel expenses, and the saving of travel time. This has important implications for both research and practical applications, as well as a high research significance and practical value.

Joint route guidance and demand management using generalized MFDs

On-line calibration of behavior parameters for behavior-consistent route guidance

The quickest transshipment of the evacuees in an integrated evacuation network topology depends upon the evacuee arrival pattern in the collection network and their better assignment in the assignment network with appropriate traffic route guidance, destination optimization, and an optimal route. In this work, the quickest transshipment aspect in an integrated evacuation network topology is revisited concerning a transit-based evacuation system. Appropriate collection approaches for the evacuees and their better assignment to transit vehicles for their quickest transshipment in an embedded evacuation network are presented with their solution strategies.

This correspondence presents an optimal guidance algorithm which takes into account the driver's compliance to route advice. The optimal control consists of minimizing the travel time of guided vehicles using a queue-based model. The optimization is based on the use of forward dynamic programming and decomposition-coordination methods. Driver's compliance is modeled by a linear function of the guidance advice (control). An assessment of the criteria degradation induced by nonperfect compliance is performed. Results show that this degradation of the criteria is low in fluid traffic conditions but high in congested situations. Thus human factors cannot be neglected in route guidance systems.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>