Route Planning for Autonomous Driving Based on Traffic Information via Multi-Objective Optimization

Electric Vehicles (EVs) is a promising transportation to alleviate traffic congestion and pollution problems and its route planning can save the limited energy. However, designing a reliable route planning strategy to achieve optimal route remains a challenging problem, especially when various objectives of both energy consumption and time cost are taken into consideration. Therefore, based on the traffic signal control technologies in urban areas and an EV energy consumption model, we formulate EV route planning problem as a multi-objective optimization problem and propose a Green wave band-based Multi-objective Ant Colony Optimization (G-MACO) algorithm to solve it. Moreover, relying on the field measurement data, the graphical model of urban road network containing relevant weights is also analyzed. Finally, simulation results show that the proposed algorithm can achieve a trade-off between energy consumption and time cost to realize the multi-objective optimization route planning.

Cairo is experiencing traffic congestion that places it among the worst in the world. Obviously, it is difficult if not impossible to solve the transportation problem because it is multi-dimensional problem but it's good to reduce this waste of money and the associated waste of time resulting from congestion. One way to accomplish this is to provide driver or passenger with current traffic information throughout their trip . Travel time prediction is becoming increasingly important and it is one of the most important traffic information for both drivers and passengers. It is difficult to measure the travel time directly so the present study estimates the travel time using the speed. In this paper we present a model based approach for travelling time prediction. It will provide both passenger or driver with the fastest routes depending on the travel time. The proposed method uses DSmT (DezertSmarandache Theory) as a fusion technique and Artificial Neural Network as mining tool . The estimates are corroborated using actual values and the results show the model performing well and gave us acceptable prediction.

The paper introduces the experiment of probe car based traffic information system experiment in Shenyang of China. It is done for evaluation of probe car system for big city, and aimed to traffic information service for mobile phone users in China. About 10000 taxies running on roads send GPS data in real time to the data base of the traffic information center. In the traffic information center, the probe information system matches the location of taxies on the digital map, calculates the average running speed and travel time on road links, and distributes the traffic information to users frequently through web and WAP portals. Test cars run on roads to evaluate the accuracy of traffic information from probe car system in 2008. The result proves the feasibility of real-time traffic information service by using probe car system based on huge amount of GPS data.

This paper has three major contributions. First, a vehicular ad-hoc network (VANET)-based A* (VBA*) route planning algorithm is proposed to calculate the route with the shortest travelling time or the least oil consumption, depending on two real-time traffic information sources. The first one is the recorded traffic information of the road segment that the vehicle has passed through. This traffic information is further exchanged between vehicles when they enter the transmission range of IEEE 802.11p wireless link in the VANET. The second one is the traffic information provided by Google Maps. Then, a GPS navigation app is implemented on the Android platform to realize the VBA* route planning algorithm. Finally, simulations for six route planning algorithms are executed by the well-known VANET simulator, i.e., The ONE. In summary, VBA* achieves significant reductions on both the average travelling time and oil consumption of the planned route, as compared to traditional route planning algorithms.

With the widespread adoption of automated traffic sensors, the increase of computer processing power, and the evolution and growth of the global Internet, our ability to monitor traffic and convey traffic information in real-time has been dramatically advanced. To the authors' knowledge, most of the current traffic information systems mainly provide general traffic flow information. Vehicle classification data, such as truck volumes, that should be key inputs for good transportation planning, freight mobility analysis, roadway geometric and structural design and traffic control and operation, are often excluded or only roughly estimated. To address this problem, this paper describes the design and implementation of a traffic monitoring and information system recently developed at the University of Washington that visually conveys speed and vehicle classification information, obtained by processing real-time single-loop measurements, to the general public through the client/server computer architecture. The development of this system makes real-time monitoring of truck volume data on a freeway network possible.

. Aiming at the characteristics of Electric Vehicles (EVs) combined with transportation and mobile load, and their charging and driving behaviors will interact with the traffic system and power grid system. On the basis of this, a route planning and charging navigation strategy for EVs based on real-time traffic information and grid information was presented in this paper. First, a dynamic traffic network model was established according to the time-varying feature of traffic information. And also, the road resistance model with road segment impedance and intersection node impedance was proposed depending on the attribute of urban roads. Furthermore, based on the establishment of traffic network model, distribution network model and single EV model, a multi-objective optimization function integrated with the road travel time, the charging station load and the quantity of vehicles entering into the station was determined, which was solved to recommend the optimal driving and charging paths by dynamic Dijkstra dynamic search algorithm. At last, the actual road network of a certain zone in Nanjing was selected as an example to analyze the spatial-temporal distribution of EV charging load, and evaluated the impact of its charging and driving on traffic network and distribution network. Simulation results demonstrate its effectiveness and feasibility.

Micro-blogging is an emerging form of communication and became very popular in recent years. Micro-blogging services allow users to publish updates as short text messages that are broadcast to the followers of users in real-time. Twitter is currently the most popular micro-blogging service. It is a rich and real-time information source and a good way to discover interesting content or to follow recent developments. Additionally, the updates published on Twitter public timeline can be retrieved through their API. A significant amount of traffic information exists on Twitter platform. Twitter users tweet when they are in traffic about accidents, road closures or road construction. With this in mind, this paper presents a system that extracts traffic information from Twitter to be used in route planning. Route planning is of increasing importance as societies try to reduce their energy consumption. Furthermore, route planning is concerned with two types of constraints: stable, such as distance between two points and temporary such as weather conditions, traffic jams or road construction. Our system attempt to extract these temporary constraints from Twitter. We train Naive bayes, Maxent and SVM classifiers to filter non relevant traffic. We then apply NER on traffic tweets to extract locations, highwaysand directions. These extracted locations are then geocoded and used in route planning to avoid routes with traffic jams.

With the development of the economy and the acceleration of urbanization, traffic congestion has become a worldwide problem. Advances in mobile Internet and sensor technologies have increased real-time data sharing, providing a new opportunity for urban route planning. However, due to the difficulty of handling complex global information, making correct decisions in large-scale and complex traffic environments is a problem that urgently needs to be solved. In this paper, a multiple route planning model (multi-route dynamic programming (DP) model) is proposed to solve the urban route planning problem with traffic flow information. In particular, we adopt the DP algorithm in this model, design a reward function suitable for urban path planning problems, and generate multiple routes based on the Q values. In addition, we design different scenarios using real-world road networks to test our model. Through the experiments, we demonstrate that our model has the potential to yield optimal results under large-scale scenarios with high efficiency. The advantages of integrating the distance contribution index (DCI) in the reward function are also elaborated. Moreover, our model can provide alternative routes to divert traffic from the optimal route, thus mitigating the congestion drift problem.

It is essential for transportation management centers to establish a network of fixed and mobile sensors to collect traffic information of highway network, especially for very important links with frequent traffic events. Emerging Unmanned Aerial Vehicles (UAVs), it is introduced as a mobile sensor to collect road traffic information and its cruise route planning problem is researched based on the highway network physical structure. First, according to existing traffic data, a method used to calculate the link importance degree index is proposed, and the index is used to evaluate the link's information. Second, a multiobjective optimization model is proposed, its aim is to minimize the total cruise time under detecting as many important links as possible and minimize the information value undetected by UAVs, and the fuzzy operator is introduced to the constraint conditions. Finally, a case study is used to demonstrate the feasibility and effectiveness of proposed model about UAVs’ route planning.

Common bus route navigation system provides distance and transferring information based on the user-determined start point and end point on the map. This paper presents the research and experiment of an advanced bus route navigation system in Beijing, which can additionally provide travel time information from start to end. The travel time is predicted based on statistical and real-time traffic information. In Beijing, about 10000 taxies send GPS data to a traffic information center to generate real-time traffic data, and the center accumulates the historical traffic data for generating statistical database. Field test of our system was carried out in January 2007, and the experiment result shows the real-time and statistical traffic information can be used not only for car navigation system, but also for public transit service. In our experiment, the average error rate of predicted travel time is about 10%, which is comparable to car navigation system.

Most of the traffic information considers a single item like travel time or delay. In the present work, enhanced traffic information displaying instantaneous travel time and its variation from the previous interval to the present, is considered. An initial investigation is made on the effectiveness of such traffic information on route choice behavior of trip makers by valuation of attributes of the traffic information. Taking a case study of two urban corridors in the Kolkata metro city, India, the valuation is done separately for private car and taxi trip makers. The stated preference (choice based) data collected from trip makers are analyzed using both multinomial logit (MNL) and mixed logit (ML) modeling techniques. Assuming sparsely used constrained triangular distribution of random parameters, two different types of ML model are developed: one with independent choice sets and the other one by accounting heterogeneity around the mean of random parameter(s). Both family income and trip purpos...

Energy shortage and air pollution is an important problem facing the world. One of the effective ways to mitigate it is to develop hybrid vehicles and other energy resource vehicles. However, traffic decision information needed by hybrid vehicle energy management system has become the major bottleneck to restricting hybrid vehicle's popularization, which shows the characteristics of multi-source, time varying and fuzzy. Hybrid vehicle Energy management problem using multi- source traffic information cycle of s is taken as the aim, numerical simulations and laboratory tests are the two methods to solve it in this project. First, dynamic extraction mechanism of multi-source traffic information, such as dynamic link travel time and vehicle path are studied based on support vector regression theory and quantum ant colony algorithm. And then, Markov theory is adopted to study the system level energy optimization modeling of hybrid vehicle energy management system based on multi-source traffic information. Finally, using multi-source traffic information, quantitative control model of energy optimization performance is constructed .This topic is aim to reveal the energy optimal law in hybrid vehicle energy management system using multi-source traffic information, which would lay the theoretical and technical basis for developing new intelligent energy management system suitable for the conditions of multi-source traffic information.

It is widely acknowledged that traffic information has the potential of increasing the reliability in road networks and in alleviating congestion and its negative environmental and societal side effects. However, for these beneficial collective effects to occur, reliable and accurate traffic information is a prerequisite. Building on previous research, this article presents a reliable framework for online travel time prediction for freeways, which could, for example, be used to generate traffic information messages on so-called dynamic route information panels on freeways. Central in this framework is a so-called state-space neural network (SSNN) model, which learns to predict travel times directly from data obtained from real time traffic data collection systems. In this article we show that by using an ensemble of SSNN models also a measure for the reliability of each prediction can be produced. This enables traffic managers to monitor in real time the reliability of this system without actually measuring travel times.

In order to effectively improve the problems existing in the current road traffic information acquisition device, such as too much equipment, low utilization ratio, serious information overlap and low detection rate, the integrated design and transformation of the traditional traffic information acquisition device is carried out, and a new type of traffic information acquisition device, the integrated traffic information detector, which integrates radar acquisition technology, video acquisition technology and Radio Frequency Identification (RFID) is designed. The integrated traffic information detector of lightning network can not only collect the intersection required by the road comprehensively, accurately and in real time, but also through the multi-source data fusion processing of the information collected by radar, video and RFID reader. Through information, and can adapt to a variety of complex detection environment. At the same time, it also fully considers the general direction of traffic in the future fifth generation mobile communication technology (5G: 5th-Generation) environment, equipped with wireless network module suitable for 5G, combined with the high capacity, low delay and high reliable high speed transmission rate under the future 5G network, to further promote the popularization and development of vehicle networking technology in the future.

In recent years, automated vehicles have de-veloped rapidly, and the safety of autonomous vehicles should be tested and verified before they can enter the mar-ket. The evaluation of the complexity of driving tasks is one of the important foundations for the design of test scenarios and the evaluation of safety tests for automated vehicles. The driving tasks were divided into a series of operation steps, and the five metrics for the complexity of the number of driving operation steps, the complexity of driving operation logic, the complexity of traffic environment infor-mation, the complexity of decision-making, and the complexity of motion planning and track controlling were de-termined through the analysis of the complexity of the driving tasks of automated vehicles. By establishing behavior control charts or information graphs, first-order or second-order entropy was used to assess each complexity index, and the analytic hierarchy process method was used to deter-mine the weighting of each index. Finally, the complexity of the driving task was get by weighting the five indicators in the form of Euclidean norm, which can be used to assess the complexity of the driving task of automated vehicles.