Edge-centric trust management in vehicular networks

A major objective of vehicular networking is to improve road safety and reduce traffic congestion. The experience of individual vehicles on traffic conditions and travel situations can be shared with other vehicles for improving their route planning and driving decisions. Nevertheless, the frequent occurrence of adversary vehicles in the network may affect the overall network performance and safety. These vehicles may behave intelligently to avoid detection. To effectively control and monitor such security threats, an efficient Trust Management system should be employed to identify the trustworthiness of individual vehicles and detect malicious drivers which is the major focus of this work. We propose a hybrid solution, which integrates Edge Computing and Multi-agent modeling in a Trust Management system for vehicular networks. The proposed solution also aims to overcome the limitations of the two commonly utilized approaches in this context: cloud computing and Peer-to-Peer (P2P) networking. Our framework has a set of features that make it an efficient platform to address the major security challenges in vehicular networks including latency, scalability, uncertainty, data accessibility, and malicious behavior detection. Performance of the approach is evaluated by simulating a realistic environment. Experimental results show that the proposed approach outperforms similar approaches from literature for various performance indicators.     

Towards High-Resolution First-Best Air Pollution Tolls

In this paper, an approach is presented to calculate high-resolution first-best air pollution tolls with respect to emission cost factors provided by Maibach et al. (2008). Dynamic traffic flows of a multi-agent transport simulation are linked to detailed air pollution emission factors. The monetary equivalent of emissions is internalized in a policy which is then used as a benchmark for evaluating the effects of a regulatory measure—a speed limitation to 30 km / h in the inner city of Munich. The calculated toll, which is equal to simulated marginal costs in terms of individual vehicle attributes and time-dependent traffic states, results in average air pollution costs that are very close to values in the literature. It is found that the regulatory measure is considerably less successful in terms of total emission reduction. It reduces emissions of urban travelers too strongly while even increasing the emissions of commuters and freight, both leading to a increase in deadweight loss. That is, the regulatory measure leads to higher market inefficiencies than a “do-nothing” strategy: too high generalized prices for urban travelers, too low generalized prices for commuters and freight. Finally, long-term changes in the vehicle fleet fuel efficiency are assumed as a reaction to the Internalization policy. The results indicate, however, that the long-term effect of emission reduction is dominated by the short-term reactions and by the assumed improvement in fleet fuel efficiency; the influence of the resulting route and mode choice decisions turns out to be relatively small.     

TEMMS: an integrated package for modelling and mapping urban traffic emissions and air quality

The Traffic Emission Modelling and Mapping Suite (TEMMS) is a program designed to provide detailed estimates of vehicle emissions on urban road networks, and so act as a precursor to urban air quality modelling. TEMMS is a module of the “Quantifiable City”, a more extensive model designed to address questions relevant to urban sustainability. Within the Quantifiable City model, TEMMS interfaces with SATURN, a traffic assignment model, and the Airviro or ADMS–Urban pollutant dispersion models, to calculate spatially defined pollutant concentrations for given traffic, meteorological and stationary source emission inputs. TEMMS also contains an integral model, ROADFAC, which calculates emissions of gases and particulates from vehicles using SATURN traffic or vehicle count data. TEMMS integrates these models via a database exchanger, the MapInfo geographic information system, and a custom-built Windows-based graphical user interface, allowing modelling and mapping of link-based vehicle flow and emissions, and grid-based air quality. TEMMS applications include emission and air quality mapping, evaluation of associated transport policies and scenarios, and preparation of inputs to other (e.g., epidemiological) models. Within this context, TEMMS has generated considerable interest from potential end-users in Local Authorities and air quality management support services. Validation of the integrated model is discussed, and an example application of TEMMS to a large UK city presented.     

Designing large‐scale interactive traffic animations for urban modeling

Designing and optimizing traffic behavior and animation is a challenging problem of interest to virtual environment content generation and to urban planning and design. While some traffic simulation methods have appeared in computer graphics, most related systems focus on the design of buildings, roads, or cities but without explicitly considering urban traffic. To our knowledge, our work provides the first interactive approach which enables a designer to specify a desired vehicular traffic behavior (e.g., road occupancy, travel time, emissions, etc.) and the system will automatically compute what realistic 3D urban model (e.g., an interconnected network of roads, parcels, and buildings) yields the specified behavior. Our system both altered and improved traffic behavior in novel procedurally‐generated cities and in road networks of existing cities. Our urban models contain up to 360 km of roads, 300,000 vehicles, and typically cover four hours of simulated peak traffic time. The typical editing session time to “paint” a new traffic pattern and to compute the new/changed urban model is two to five minutes.     

Distributed Model Predictive Control Method for Optimal Coordination of Signal Splits in Urban Traffic Networks

Coordination and control approaches based on model predictive control (MPC) have been widely investigated for traffic signal control in urban traffic networks. However, due to the complex non‐linear characters of traffic flows and the large scale of traffic networks, a basic challenge faced by these approaches is the high online computational complexity. In this paper, to reduce the computational complexity and improve the applicability of traffic signal control approaches based on MPC in practice, we propose a distributed MPC approach (DCA‐MPC) to coordinate and optimize the signal splits. Instead of describing the dynamics of traffic flow within each link of the traffic network with a simplified linear model, we present an improved nonlinear traffic model. Based on the nonlinear model, an MPC optimization framework for the signal splits control is developed, whereby the interactions between subsystems are accurately modeled by employing two interconnecting constraints. In addition, by designing a novel dual decomposition strategy, a distributed coordination algorithm is proposed. Finally, with a benchmark traffic network, experimental results are given to illustrate the effectiveness of the proposed method.     

A complementary formulation for traffic equilibrium problem with a new nonadditive route cost

Improving transportation system is essential for all people in each city since transport plays a very important role.Using mathematical programming approach transport problem is an effective way to improve transportation system.In this paper,the traffic equilibrium problem(TEP)with a general nonadditive route cost function is studied.We formulate the route cost function for each route as a disutility function,which can evaluate route cost function flexibly and analyze the route toll conveniently.Furthermore...     

一种车载自组织网络中交通拥塞自适应的出行计划方案

交通堵塞现象越来越威胁正常的城市交通,针对选择最短路径的出行方案往往不能取得最短的出行时间的现象,提出了一种交通拥塞自适应的出行计划方案.该方案克服了现有方案在规划出行路线时未能考虑行车过程中实际交通状况的缺点,根据车辆在各路段行驶的平均通过时间来判断路段当前的拥塞状况,并动态优化行车路线,从而提高交通效率.仿真实验表明该方案能够自适应路段的交通拥塞,根据当前拥塞状况重新优化行车路线,从而缩短平均行车时间.     

A macroscopic model with the advantages of microscopic model: A review of Cell Transmission Model’s extensions for urban traffic networks

This paper reports a review of the extensions and application of the Cell Transmission Model (CTM). Those extensions are models able to simulate complex urban traffic dynamics with all the advantages of macroscopic and microscopic urban traffic model. Over the past few years researchers have been trying to increase the level of detail by extending CTM and introducing new formulations to improve the application of the model in urban traffic. The authors classified the papers while taking into consideration all those factors characterizing the urban traffic, arterial and intersection traffic flow in particular. One of the primary goals of transport research is to develop a general framework of urban traffic networks that might be applied from a realistic point of view. Recent studies about traffic simulations have shown that, among various macroscopic simulation models, the CTM has the potential to achieve this objective. We have also reported our model the CTM_UT that improves the CTM for Urban Traffic. We believe that it is possible to apply this model to ITS application, hence increase the accuracy of the macroscopic model while maintaining the computational advantages and provide an accurate prediction of travel time approach.     

NCTUns tool for wireless vehicular communication network researches

Several goals such as improving road safety and increasing transport efficiency are being pursued in intelligent transportation systems (ITS). Wireless vehicular communication is one technology to achieve these goals. Conducting vehicular experiments on the roads is an approach to studying the effectiveness of wireless vehicular communication. However, such an approach is costly, hard-to-control (repeat), dangerous, and infeasible when many vehicles and people are involved in the field trial. In contrast, the simulation approach does not have these problems. It is a very useful approach and complements the field trial approach. This paper presents NCTUns, an open source integrated simulation platform, for wireless vehicular communication network researches. This tool tightly integrates network and traffic simulations and provides a fast feedback loop between them. Therefore, a simulated vehicle can quickly change its driving behavior such as moving speed and direction when it receives a message from the wireless vehicular communication network. This capability is required by several novel ITS applications such as active collision avoidance systems. In this paper, we present the design, implementation, validation, and performance of this tool.     

Synthesising carbon emission for mega-cities: A static spatial microsimulation of transport CO2 from urban travel in Beijing

Developing low carbon cities is a key goal of 21st century planning, and one that can be supported by a better understanding of the factors that shape travel behaviour, and resulting carbon emissions. Understanding travel based carbon emissions in mega-cities is vital, but city size and often a lack of required data, limits the ability to apply linked land use, transport and tactical transport models to investigate the impact of policy and planning interventions on travel and emissions. Here, we adopt an alternative approach, through the development of a static spatial microsimulation of people’s daily travel behaviour. Using Beijing as a case study, we first derive complete activity-travel records for 1026 residents from an activity diary survey. Then, using the 2000 population census data at the sub-district level, we apply a simulated annealing algorithm to create a synthetic population at fine spatial scale for Beijing and spatially simulate the population’s daily travel, including trip distance and mode choice at the sub-district scale. Finally, we estimate transport CO₂ emission from daily urban travel at the disaggregate level in urban Beijing.     

Dynamic User Equilibrium Model for Combined Activity-Travel Choices Using Activity-Travel Supernetwork Representation

Integrated urban transportation models have several benefits over sequential models including consistent solutions, quicker convergence, and more realistic representation of behavior. Static models have been integrated using the concept of Supernetworks. However integrated dynamic transport models are less common. In this paper, activity location, time of participation, duration, and route choice decisions are jointly modeled in a single unified dynamic framework referred to as Activity-Travel Networks (ATNs). ATNs is a type of Supernetwork where virtual links representing activity choices are added to augment the travel network to represent additional choice dimensions. Each route in the augmented network represents a set of travel and activity arcs. Therefore, choosing a route is analogous to choosing an activity location, duration, time of participation, and travel route. A cell-based transmission model (CTM) is embedded to capture the traffic flow dynamics. The dynamic user equilibrium (DUE) behavior requires that all used routes (activity-travel sequences) provide equal and greater utility compared to unused routes. An equivalent variational inequality problem is obtained. A solution method based on route-swapping algorithm is tested on a hypothetical network under different demand levels and parameter assumptions.     

Traffic video‐based intelligent traffic control system for smart cities using modified ant colony optimizer

Road traffic congestion is a serious problem in today's world and it happens because of urbanization and population growth. The traffic reduces the transport efficiency in the city, increases the waiting time and travel time, and also increases the usage of fuel and air pollution. To overcome these issues this papers propose an intelligent traffic control system using the Internet of Vehicles (IoV). The vehicles or nodes present in the IoV can communicate between themselves. This technique helps in determining the traffic intensity and the best route to reach the destination. The area of study used in this paper is Vellore city in Tamilnadu, India. The city map is separated into many segments of equal size and Ant Colony Algorithm (AOC) is applied to the separated maps to find the optimal route to reach the destination. Further, Support Vector Machine (SVM) is used to calculate the traffic density and to model the heavy traffic. The proposed algorithm performs better in finding the optimal route when compared to that of the existing path selection algorithms. From the results, it is evident that the proposed IoV‐based route selection method provides better performance.     

INTEGRATED MODELING OF ROAD TRAFFIC EMISSIONS: APPLICATION TO LISBON AIR QUALITY MANAGEMENT

The application of three distinct modeling tools to deal with Lisbon atmospheric problems is presented. Information and forecasting system for private and public transport (VISUM), Transport Emission Model for Line Sources (TREM) and Variable Dispersion (VADIS) models were used to characterize the traffic fluxes, to quantify the emission amounts, and finally to evaluate the air quality in a specific area of the city characterized by intense traffic respectively. The results show the benefit of integrated use of the modeling tools VISUM and TREM to estimate the atmospheric emissions induced by traffic. On the other hand, the results obtained with VADIS are in acceptable agreement with the measured air quality data.     

基于物联网的城市交通污染气体排放监控预警系统

交通污染气体排放是影响城市环境质量的主要因素之一.融合GIS、GPRS以及GPS技术,本系统通过传感器动态跟踪监测点附近的道路交通污染气体排放量,以及与之相关的影响因素,如:交通流、道路线形、天气等,利用K-近邻算法挖掘其内在联系,结合实际交通状况,构建能够准确反映交通污染气体在时空上的排放量分布预警模型,为如何降低交通污染排放提供决策支持依据.实验结果表明,本系统能准确预测交通污染气体的排放分布趋势,为交通管理部门进行道路交通管理提供依据.     

Understanding urban structures and crowd dynamics leveraging large-scale vehicle mobility data

A comprehensive understanding of city structures and urban dynamics can greatly improve the efficiency and quality of urban planning and management, while the traditional approaches of which, such as manual surveys, usually incur substantial labor and time. In this paper, we propose a data-driven framework to sense urban structures and dynamics from large-scale vehicle mobility data. First, we divide the city into fine-grained grids, and cluster the grids with similar mobility features into structured urban areas with a proposed distance-constrained clustering algorithm (DCCA). Second, we detect irregular mobility traffic patterns in each area leveraging an ARIMA-based anomaly detection algorithm (ADAM), and correlate them to the urban social and emergency events. Finally, we build a visualization system to demonstrate the urban structures and crowd dynamics.We evaluate our framework using real-world datasets collected from Xiamen city, China, and the results show that the proposed framework can sense urban structures and crowd comprehensively and effectively.     

Angular and Metric Distance in Road Network Analysis: A nationwide correlation study

There is an epistemological divide in the field of road network analysis, concerning the way network distance should be conceptualized. On one hand, the generality of studies in the field adopt metric distance as a self-evident choice. On the other, space syntax studies adopt a different distance concept, namely that of angular distance, which ignores actual physical lengths. Theoretically, these two distance concepts imply quite different assumptions. Analytically, they produce also different results.In this paper we assess the differences between these two network distance concepts, using a model of the UK's complete road network (2,031,971 nodes) and a very large dataset of vehicular movement counts (20,752 locations). We describe the statistical associations between observed vehicular flows and the betweenness centrality of the road-network nodes where such flows were measured, the latter calculated with metric and angular distance functions, across a number of increasing radii. Relations to road capacity are also discussed in principal roads where this is known.The geographical comprehensiveness of our model and the size of our movement sample allow us to state, with unprecedented statistical validity, the clear outperformance of angular distance over metric distance, in what concerns the strength of the studied correlations. This is true for all types of roads (urban and non-urban) and for all motorized vehicles, representing 99.05% of the total traffic; the remaining 0.95% correspond to pedal cycles, which yielded inconclusive results. We also demonstrate the existence of two distinct regimes of association between movement and centrality, conspicuous in cities but altogether absent in non-urban areas, which represent new evidence supporting space syntax's dual model of urban form.     

A general framework for modeling shared autonomous vehicles with dynamic network-loading and dynamic ride-sharing application

Shared autonomous vehicles (SAVs) could provide low-cost service to travelers and possibly replace the need for personal vehicles. Previous studies found that each SAV could service multiple travelers, but many used unrealistic congestion models, networks, and/or travel demands. The purpose of this paper is to provide a method for future research to use realistic flow models to obtain more accurate predictions about SAV benefits. This paper presents an event-based framework for implementing SAV behavior in existing traffic simulation models. We demonstrate this framework in a cell transmission model-based dynamic network loading simulator. We also study a heuristic approach for dynamic ride-sharing. We compared personal vehicles and SAV scenarios on the downtown Austin city network. Without dynamic ride-sharing, the additional empty repositioning trips made by SAVs increased congestion and travel times. However, dynamic ride-sharing resulted in travel times comparable to those of personal vehicles because ride-sharing reduced vehicular demand. Overall, the results show that using realistic traffic flow models greatly affects the predictions of how SAVs will affect traffic congestion and travel patterns. Future work should use a framework such as the one in this paper to integrate SAVs with established traffic flow simulators.     

Adaptive green traffic signal controlling using vehicular communication

The importance of using adaptive traffic signal control for figuring out the unpredictable traffic congestion in today’s metropolitan life cannot be overemphasized. The vehicular ad hoc network (VANET), as an integral component of intelligent transportation systems (ITSs), is a new potent technology that has recently gained the attention of academics to replace traditional instruments for providing information for adaptive traffic signal controlling systems (TSCSs). Meanwhile, the suggestions of VANET-based TSCS approaches have some weaknesses: (1) imperfect compatibility of signal timing algorithms with the obtained VANET-based data types, and (2) inefficient process of gathering and transmitting vehicle density information from the perspective of network quality of service (QoS). This paper proposes an approach that reduces the aforementioned problems and improves the performance of TSCS by decreasing the vehicle waiting time, and subsequently their pollutant emissions at intersections. To achieve these goals, a combination of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications is used. The V2V communication scheme incorporates the procedure of density calculation of vehicles in clusters, and V2I communication is employed to transfer the computed density information and prioritized movements information to the road side traffic controller. The main traffic input for applying traffic assessment in this approach is the queue length of vehicle clusters at the intersections. The proposed approach is compared with one of the popular VANET-based related approaches called MC-DRIVE in addition to the traditional simple adaptive TSCS that uses the Webster method. The evaluation results show the superiority of the proposed approach based on both traffic and network QoS criteria.