Vehicular emissions make significant contribution to the total ambient airborne pollutants. Global warming and human health concerns are motivating researchers to come out with newer ways of controlling air pollution effectively. On the other hand, there are significant challenges in complying with current and upcoming vehicle emission regulations, which are quite stringent. It is therefore quite important to monitor vehicular emissions closely, which can facilitate adopting effective control measures when necessary, and also in predicting the impact of vehicular emissions on ambient air quality. Traditionally, dynamometers (both engine and chassis) testing has been used extensively to measure and monitor vehicular emissions, and the database generated has been used as input in modeling the traffic-related air quality impact. Even though standard driving cycles are followed in dynamometer tests attempting to closely replicate real-world driving conditions, they may not necessarily represent actual real-world driving conditions and emissions thereof. Therefore, in recent years, significant scientific efforts have been directed to measure and analyze real-world driving emissions (RDE) from vehicles. In this paper, the state-of-the-art techniques and methods for vehicular emissions monitoring under real-world driving conditions are reviewed and discussed in detail. Different vehicle emissions monitoring methods are presented in comparison to dynamometer-based measurements. Several influencing factors which affect on-road and in laboratory measurements are identified and discussed. Potential applications of different emission control strategies are reviewed. Finally, guidelines are formulated for effective vehicular emissions monitoring, and to minimize discrepancies between on-road and laboratory based measurements, in order to have a sustainable road transport system in future.

Real-world automotive emissions: Monitoring methodologies, and control measures

A robust traffic rerouting system is important in traffic management, alongside an accurate traffic simulation model. However, missing data continues to be a problem as it will inevitably cause errors in predicting the congestion levels, resulting in a less efficient rerouting. The lack of a realistic traffic simulation also serves to hamper the development of a better traffic management system. As such, this paper aims to address both problems by proposing three solutions: (i) a traffic simulation that would model a live-traffic, (ii) a pheromone-based, neural network traffic prediction and rerouting system, and (iii) a missing data handling method utilising weighted historical data method named Weighted Missing Data Imputation (WEMDI). The traffic simulation model was benchmarked using Google Maps rerouting system. WEMDI was tested by comparing the performance of the rerouting system with and without WEMDI’s integration for various levels of missing data. The results showed that the traffic simulation model displayed a high correlation to that of Google Maps, and the WEMDI-integrated system displayed 38% to 44% improvement in the related traffic factors, when compared to a situation with no rerouting system in place, and up to 19.39% increase in performance compared to the base rerouting system for missing data levels of 50%. The WEMDI system also displayed robustness in routing other locations, displaying a similarly high performance.

A neural network approach for traffic prediction and routing with missing data imputation for intelligent transportation system

Abstract Traffic modelling and simulation is one of the frequent tools used in road infrastructure design. Software tools designed for traffic simulations are an important supportive tool in decision-making and in choosing the optimal solution. The aim of this paper is to introduce the application of the VISSIM program, in the design and testing a model of the traffic-light-controlled intersection. The traffic on the selected congested intersection is modelled and simulated first for the current state, then for two models with modifications that are to increase the throughput of the intersection. The monitored criterion of the intersection throughput is the length of queues. Both adjustments have led to a significant reduction of the number of vehicles waiting in direction of the greatest congestions. In the first model, the average line length was reduced by 75%, and in the second model, the modifications lead to a fluent passage of right-turn vehicles and a significant reduction in vehicle lines for other directions.

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Design and evaluation of a new intersection model to minimize congestions using VISSIM software

Automatic traffic modelling for creating digital twins to facilitate autonomous vehicle development

Continuous developments in automotive technology brought about the reality of having an increasing number of autonomous vehicles (AV) on roadways soon. However, this advancement comes with the uncertainty of how the interaction between the autonomous and non-autonomous (non-AV) vehicles will play out. Using traffic simulators to model both AVs and non-AVs, and studying the effect of AVs on roadway mobility can help researchers in learning what to expect in real life. In this paper, an open-source simulator called Simulation of Urban Mobility (SUMO) was used to develop AVs by modifying Intelligent Driver Model (IDM) car following and LC2013 lane changing models. Then, three actual roadways in Columbus, Ohio were modeled, and 40 simulations were run for each traffic network. The first set of simulations focused on the impacts that AVs have on vehicle roadway mobility with non-AVs when all the AVs have the same autonomy level. The second set of simulations had mixed AV autonomy levels (SAE Level 2, 3 and 4) with mixed AV and non-AV traffic. The analysis revealed that mobility of AVs and non-AVs increased generally as autonomy level increased. When the AV autonomy levels were mixed, the non-AVs experienced lower average speeds and decreased roadway mobility.

Simulating the Effect of Autonomous Vehicles on Roadway Mobility in a Microscopic Traffic Simulator

Traffic simulation models and software tools have been developed for the purpose of traffic modelling, planning and to analyse different strategies in traffic control during simulations. Traffic simulation models and tools are increasingly used in real-time for traffic management with the use of area-wide online traffic data. A comparison of 17 simulation software tools has been conducted by analysing scientific papers and technical specifications. An online survey with the focus on realized functionalities and planned improvements has been conducted together with traffic simulation tool developers and product managers. Particular emphasis was placed on the flexibility and adaptively of real-time simulation solutions in the context of heterogeneous road networks (urban, interurban, rural) and other special requirements in non-mainstream regions. It has become apparent that simulation software tools have many challenges in the application of simulating road conditions of complex heterogeneous road transportation networks and heterogeneous traffic with a small amount of real-time data.

Trends in Real-time Traffic Simulation

Traffic models and simulation tools are increasingly used for traffic management of wide-area road networks by using real-time data. Originally, these systems were developed for the purpose of traffic modelling, planning and to compare different traffic control strategies during simulations. Considering the growing field of applications and the increase of supported functionalities, a comparison study of 12 simulation software tools available on the market has been conducted. Existing studies, product reviews and technical product specification documents have been analysed and additionally product developers were asked to participate in an on-line survey. As a result a review report has been drawn up. It became apparent that there are just a few traffic simulators which can be used for real-time simulation of wide-area road transportation networks.

Comparison Study of Software Tools for Online Traffic Simulation Supporting Real-time Traffic Management of Road Networks

In this study, the applicability of the Austrian national transport graph “GIP.at”, which is provided and constantly updated by Austrian public authorities, for a location-allocation problem-solving approach is examined. For this purpose ESRI’s ArcGIS Network AnalystTM was applied on the part of the graph which represents the roads of the Province of Upper Austria. Therefore, the geometric network and its attributes are compared with other provider’s geographical data. In a second step the street network graphs are used to solve a location-allocation problem in a case study about an Upper Austrian food retailer. The comparison of the results shows, that using diverse geographic data leads to the same facility locations and allocations. Subsequently, the applied geographic data are investigated in more detail. Missing data attributes which are highly relevant for heavy goods vehicles (HGV) routing like vehicle specific restrictions (height, width, weight) or HGV driving bans are depicted.

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Andreas Meingast

Papers

Trends in Real-time Traffic Simulation

Comparison Study of Software Tools for Online Traffic Simulation Supporting Real-time Traffic Management of Road Networks

Analysis of the applicability of the public Upper Austrian transport graph for solving a location-allocation problem