Vehicle re-routing strategies for congestion avoidance

Traffic congestion causes driver frustration and costs billions of dollars annually in lost time and fuel consumption. This dissertation introduces a cost-effective and easily deployable vehicular re-routing system that reduces the effects of traffic congestion. The system collects real-time traffic data from vehicles and road-side sensors, and computes proactive, individually tailored re-routing guidance, which is pushed to vehicles when signs of congestion are observed on their routes. Subsequently, this dissertation proposes and evaluates two classes of re-routing strategies designed to be incorporated into this system, namely, Single Shortest Path strategies and Multiple Shortest Paths Strategies. These strategies are firstly implemented in a centralized system, where a server receives traffic updates from cars, computes alternative routes, and pushes them as guidance to drivers. The extensive experimental results show that the proposed strategies are capable of reducing the travel time comparable to a state-of-the-art Dynamic Traffic Assignment (DTA) algorithm, while avoiding the issues that make DTA impractical, such as lack of scalability and robustness, and high computation time. Furthermore, the variety of proposed strategies allows the system to be tuned to different levels of trade-off between re-routing effectiveness and computational efficiency. Also, the proposed traffic guidance system is robust even if many drivers ignore the guidance, or if the system adoption rate is relatively low. The centralized system suffers from two intrinsic problems: the central server has to perform intensive computation and communication with the vehicles in real-time, which can make such solutions infeasible for large regions with many vehicles; and driver privacy is not protected since the drivers have to share their location as well as the origins and destinations of their trips with the server, which may prevent the adoption of such solutions. To address these problems, a hybrid vehicular re-routing system is presented in this dissertation. The system off-loads a large part of the re-routing computation at the vehicles, and thus, the re-routing process becomes practical in real-time. To make collaborative re-routing decisions, the vehicles exchange messages over vehicular ad hoc networks. The system is hybrid because it still uses a server to determine an accurate global view of the traffic. In addition, the user privacy is balanced with the re-routing effectiveness. The simulation results demonstrate that, compared with a centralized system, the proposed hybrid system increases the user privacy substantially, while the re-routing effectiveness is minimally impacted

A Corridor Level GIS-Based Decision Support Model to Evaluate Truck Diversion Strategies

Increased urbanization, population growth, and economic development within the U.S. have led to an increased demand for freight travel to meet the needs of individuals and businesses. Consequently, freight transportation has grown significantly over time and has expanded beyond the capacity of infrastructure, which has caused new challenges in many regions. To maintain quality of life and enhance public safety, more effort must be dedicated to investigating and planning in the area of traffic management and to assessing the impact of trucks on highway systems. Traffic diversion is an effective strategy to reduce the impact of incident-induced congestion, but alternative routes for truck traffic must be carefully selected based on a route\u27s restrictions on the size and weight of commercial vehicles, route\u27s operational characteristics, and safety considerations. This study presents a diversion decision methodology that integrates the network analyst tool package of the ArcGIS platform with regression analysis to determine optimal alternative routes for trucks under nonrecurrent delay conditions. When an incident occurs on a limited-access road, the diversion algorithm can be initiated. The algorithm is embedded with an incident clearance prediction model that estimates travel time on the current route based on a number of factors including incident severity; capacity reduction; number of lanes closed; type of incident; traffic characteristics; temporal characteristics; responders; and reporting, response, and clearance times. If travel time is expected to increase because of the event, a truck alternative route selection module is activated. This module evaluates available routes for diversion based on predefined criteria including roadway characteristics (number of lanes and lane width), heavy vehicle restrictions (vertical clearance, bridge efficiency ranking, bridge design load, and span limitations), traffic conditions (level of service and speed limit), and neighborhood impact (proximity to schools and hospitals and the intensity of commercial and residential development). If any available alternative routes reduce travel time, the trucks are provided with a diversion strategy. The proposed decision-making tool can assist transportation planners in making truck diversion decisions based on observed conditions. The results of a simulation and a feasibility analysis indicate that the tool can improve the safety and efficiency of the overall traffic network

Intelligent and predictive vehicular networks

Seeking shortest travel times through smart algorithms may not only optimize the travel times but also reduce carbon emissions, such as CO2, CO and Hydro-Carbons. It can also result in reduced driver frustrations and can increase passenger expectations of consistent travel times, which in turn points to benefits in overall planning of day schedules. Fuel consumption savings are another benefit from the same. However, attempts to elect the shortest path as an assumption of quick travel times, often work counter to the very objective intended and come with the risk of creating a “Braess Paradox” which is about congestion resulting when several drivers attempt to elect the same shortest route. The situation that arises has been referred to as the price of anarchy! We propose algorithms that find multiple shortest paths between an origin and a destination. It must be appreciated that these will not yield the exact number of Kilometers travelled, but favourable weights in terms of travel times so that a reasonable allowable time difference between the multiple shortest paths is attained when the same Origin and Destinations are considered and favourable responsive routes are determined as variables of traffic levels and time of day. These routes are selected on the paradigm of route balancing, re-routing algorithms and traffic light intelligence all coming together to result in optimized consistent travel times whose benefits are evenly spread to all motorist, unlike the Entropy balanced k shortest paths (EBkSP) method which favours some motorists on the basis of urgency. This paper proposes a Fully Balanced Multiple- Candidate shortest path (FBMkP) by which we model in SUMO to overcome the computational overhead of assigning priority differently to each travelling vehicle using intelligence at intersections and other points on the vehicular network. The FBMkP opens up traffic by fully balancing the whole network so as to benefit every motorist. Whereas the EBkSP reserves some routes for cars on high priority, our algorithm distributes the benefits of smart routing to all vehicles on the network and serves the road side units such as induction loops and detectors from having to remember the urgency of each vehicle. Instead, detectors and induction loops simply have to poll the destination of the vehicle and not any urgency factor. The minimal data being processed significantly reduce computational times and the benefits all vehicles. The multiple-candidate shortest paths selected on the basis of current traffic status on each possible route increase the efficiency. Routes are fewer than vehicles so possessing weights of routes is smarter than processing individual vehicle weights. This is a multi-objective function project where improving one factor such as travel times improves many more cost, social and environmental factors

V2X-d: a Vehicular Density Estimation System that combines V2V and V2I Communications

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Road traffic is experiencing a drastic increase, and vehicular traffic congestion is becoming a major problem, especially in metropolitan environments throughout the world. Additionally, in modern Intelligent Transportation Systems (ITS) communications, the high amount of information that can be generated and processed by vehicles will significantly increase message redundancy, channel contention, and message collisions, thus reducing the efficiency of message dissemination processes. In this work, we present a V2X architecture to estimate traffic density on the road that relies on the advantages of combining V2V and V2I communications. Our proposal uses both the number of beacons received per vehicle (V2V) and per RSU (V2I), as well as the roadmap topology features to estimate the vehicle density. By using our approach, modern Intelligent Transportation Systems will be able to reduce traffic congestion and also to adopt more efficient message dissemination protocols.This work was partially supported by the Ministerio de Ciencia e Innovación, Spain, under Grant TIN2011-27543-C03-01, by the Fundación Universitaria Antonio Gargallo and the Obra Social de Ibercaja, under Grant 2013/B010, as well as the Government of Aragón and the European Social Fund (T91 Research Group).Barrachina Villalba, J.; Sangüesa, JA.; Fogue, M.; Garrido, P.; Martínez, FJ.; Cano Escribá, JC.; Tavares De Araujo Cesariny Calafate, CM.... (2013). V2X-d: a Vehicular Density Estimation System that combines V2V and V2I Communications. IEEE. https://doi.org/10.1109/WD.2013.6686518

Reducing non-recurrent urban traffic congestion using vehicle re-routing

Recently, with the trend of world-wide urbanization, some of the accompanying problems are getting serious, including road traffic congestion. To deal with this problem, city planners now resort to the application of the latest information and communications technologies. One example is the adaptive traffic signal control system (e.g. SCATS, SCOOT). To increase the throughput of each main intersection, it dynamically adjusts the traffic light phases according to real-time traffic conditions collected by widely deployed induction loops and sensors. Another typical application is the on-board vehicle navigation system. It can provide drivers with a personalized route according to their preferences (e.g. shortest/fastest/easiest), utilizing comprehensive geo-map data and floating car data. Dynamic traffic assignment is also one of the key proposed methodologies, as it not only benefits the individual driver, but can also provide a route assignment solution for all vehicles with guaranteed minimum average travel time. However, the non-recurrent road traffic congestion problem is still not addressed properly. Unlike the recurrent traffic congestion, which is predictable by capturing the daily traffic pattern, unexpected road traffic congestion caused by unexpected en-route events (e.g. road maintenance, an unplanned parade, car crashes, etc.), often propagates to larger areas in very short time. Consequently, the congestion level of areas around the event location will be significantly degraded. Unfortunately, the three aforementioned methods cannot reduce this unexpected congestion in real time. The contribution of this thesis firstly lies in emphasizing the importance of the dynamic time constraint for vehicle rerouting. Secondly, a framework for evaluating the performance of vehicle route planning algorithms is proposed along with a case study on the simulated scenario of Cologne city. Thirdly, based on the multi-agent architecture of SCATS, the next road rerouting (NRR) system is introduced. Each agent in NRR can use the locally available information to provide the most promising next road guidance in the face of the unexpected urban traffic congestion. In the last contribution of this thesis, further performance improvement of NRR is achieved by the provision of high-resolution, high update frequency traffic information using vehicular ad hoc networks. Moreover, NRR includes an adaptation mechanism to dynamically determine the algorithmic (i.e. factors in the heuristic routing cost function) and operational (i.e. group of agents which must be enabled) parameters. The simulation results show that in the realistic urban scenario, compared to the existing solutions, NRR can significantly reduce the average travel time and improve the travel time reliability. The results also indicate that for both rerouted and non-rerouted vehicles, NRR does not bring any obvious unfairness issue where some vehicles overwhelmingly sacrifice their own travel time to obtain global benefits for other vehicles

ALGORITHMS FOR TRAFFIC MANAGEMENT IN THE INTELLIGENT TRANSPORT SYSTEMS

Traffic jams interfere with the drivers and cost billions of dollars per year and lead to a substantial increase in fuel consumption. In order to avoid such problems the paper describes the algorithms for traffic management in intelligent transportation system, which collects traffic information in real time and is able to detect and manage congestion on the basis of this information. The results show that the proposed algorithms reduce the average travel time, emissions and fuel consumption. In particular, travel time has decreased by about 23%, the average fuel consumption of 9%, and the average emission of 10%

Congestion Propagation Based Bottleneck Identification in Urban Road Networks

© 1967-2012 IEEE. Due to the rapid development of vehicular transportation and urbanization, traffic congestion has been increasing and becomes a serious problem in almost all major cities worldwide. Many instances of traffic congestion can be traced to their root causes, the so-called traffic bottlenecks, where relief of traffic congestion at bottlenecks can bring network-wide improvement. Therefore, it is important to identify the locations of bottlenecks and very often the most effective way to improve traffic flow and relieve traffic congestion is to improve traffic situations at bottlenecks. In this article, we first propose a novel definition of traffic bottleneck taking into account both the congestion level cost of a road segment itself and the contagion cost that the congestion may propagate to other road segments. Then, an algorithm is presented to identify congested road segments and construct congestion propagation graphs to model congestion propagation in urban road networks. Using the graphs, maximal spanning trees are constructed that allow an easy identification of the causal relationship between congestion at different road segments. Moreover, using Markov analysis to determine the probabilities of congestion propagation from one road segment to another road segment, we can calculate the aforementioned congestion cost and identify bottlenecks in the road network. Finally, simulation studies using SUMO confirm that traffic relief at the bottlenecks identified using the proposed technique can bring more effective network-wide improvement. Furthermore, when considering the impact of congestion propagation, the most congested road segments are not necessarily bottlenecks in the road network. The proposed approach can better capture the features of urban bottlenecks and lead to a more effective way to identify bottlenecks for traffic improvement. Experiments are further conducted using data collected from inductive loop detectors in Taipei road network and some road segments are identified as bottlenecks using the proposed method

Analyzing the Impact of Wireless Multi-Hop Networking On Vehicular Safety

One of the core challenges of Intelligent Transportation System is the dissemination of timely and accurate vehicle information (e.g. speed, position) to geographically large distances without compromising data supply rates from immediate neighbors. This feature is critical for the design of vehicle safety and navigation applications. Single hop broadcasting is often inadequate to ensure vehicle safety when the platoon size is arbitrarily large due to its upper bound on rate and range of wireless message transmission. Existing wireless multi-hop protocols do not ensure reliable message delivery while avoiding network congestion in the shared channel. In this thesis, we make two separate but related investigations to address this challenge - (1) Analyze the impact of distance sensitive multi-hop broadcasting in realistic traffic network (2) Analyze the impact of wireless multi-hop network in vehicle safety. For investigating the first part, we used VCAST, a distance sensitive information propagation technique, in which information is forwarded at a rate that decreases linearly with distance from the source. VCAST is evaluated by using extensive simulations in ns-3, a discrete event simulator for wireless and mobile ad-hoc networks, under different density, source broadcast rates and communication range. To simulate realistic traffic movement, we used 2d grids of different sizes and used both uniform and non-uniform mobility. The results show that VCAST is scalable for - large number of vehicles and large source broadcast rates. It is further shown that successful scaling is achieved by reduced number of vehicle records transmitted per second per vehicle for varying network sizes and varying source broadcast rates. Vehicle safety messages for VCAST are piggy backed on heart beat messages and does not require any modifications to the existing vehicular communication standards. For investigating the second part, we implemented a realistic car following model and used string stability analysis as a metric for measuring vehicle safety. The basic idea is to exploit the small network propagation time in disseminating safety messages over large distances, instead of relying on just the predecessor vehicle\u27s state. This enables distant vehicles in a traffic stream to plan well in advance against rear end collisions which could lead to string instability. We also proposed one such proactive method of planning - and that is by controlling the headway time. Through extensive simulations, we obtained results for vehicle safety when some incident is detected abruptly on its course. The results show that proactive planning using multi-hop network makes the entire platoon string stable in the presence of emergency road incidents