Decision support system for accessing costs and risks of connected and autonomous vehicles as mobility service in urban contexts

A predicted increase of connected autonomous vehicles (CAVs) in our roads paved the way for new opportunities and challenges towards the promotion of sustainable mobility. However, the impacts of CAVs on the road environment and its implications are widely dependent on technological choices and public policy [1,2]. Therefore, this research (PhD Workplan) intends to assess whether CAVs could be effective mobility solutions for improving social, economic and environmental efficiency [4]. This question will be addressed by developing a decision support tool driven by comprehensive data analysis and modelling processes. The outputs achieved will integrate a tool that will support transport system’s planning and the implementation of urban strategies to introduce CAVs [3,5]. Thus, the research’s main focus encompasses the development of a model-driven decision support system (DSS) that allows assessing the costs and risks of implementing CAVs in urban context [3,4]. Three specific research objectives are assumed: I) Predicting impacts of CAVs operation in urban contexts, by analyzing cost-efficiency, transportation demand and mobility patterns considering market penetration scenarios of CAVs in Portugal; II) Conceiving a hybrid transport planning tool to assess possible restrictions to CAVs on different types of links through field data testing and simulating scenarios using a microscopic traffic model. Data will support the development of a macroscopic model for a full network assessment performance; III) Developing a multidimensional decision tool directed to a wide range of stakeholders, both from public or private sectors, to compute the benefits, costs, constraints and risks of implementing CAVs on urban mobility systems. Preliminary results from different urban arterials indicate that CAVs can have negative or negligible impacts in some urban contexts. However, if the impact on the traffic flow’s energy performance is considered into the internal car following algorithm, global energy savings of up to 12% can be achieved.publishe

Integrating environmental impacts in an intercity corridor level pricing scheme

A significant part of the transport sector externalities occurs in intercity corridors, which account for 65% of the total of the kilometers travelled in for example, Portugal (for 2017). A thorough analysis of intercity corridors characteristics has been receiving less attention compared to urban roads. The objective of this work is to propose a methodology to tackle intercity corridors issues with respect to environmental impacts. It will focus on suggesting smart and dynamic toll systems, integration of impacts in pricing schemes, and optimization of public transport fares, coupled with a scheme based on the “polluter pays” principle. This vision paper presents the main objectives and methodology of an ongoing research in which the final objective is to lead to a more efficient usage of the infrastructures. The optimization is mainly focused on an environmental perspective, which can be important for decision-makers to improve specific intercity corridor measures/policies.publishe

Exploring new ways to charge intercity mobility: impact on road traffic externalities

Around 70% of transport-related emissions in the EU (European Union) came from road transportation. A major contribution to the transport-related emission externalities comes from all the passenger car trips generated in intercity corridors. In Portugal, these trips represent 65% of the kilometers travelled and more than 55% of CO2 and NOx emissions. Portugal is the second worst country withing EU, only followed by Luxembourg, in terms of the relationship between external costs of transport and the country GDP (Gross Domestic Product)., the external costs of transport account for 7.2% of the country GDP. This work intends to assess how generalized GPS-based toll systems can reduce emissions compared with a flat-electronic collection system. The model for estimating network demand and traffic assignment is PTV VISUM. Emissions are estimated using a macroscopic methodology. The variables under study are the CO2 and NOx emissions, emissions-related external costs, total revenue, user costs. A trade-off will be performed to discuss the best strategy for different periods under study (peak and off-peak hours). Previous research efforts related to GPS-based toll collection systems do not refer to the environmental impacts of the application. These research gaps are addressed in this work by proposing a methodology focused on innovative road pricing emission-based tolls (e.g. GPS-based tolls) in intercity corridors. Simulation experiment results on a case study in Portugal comprising alternative routes of approximately 60km show that two different strategies are recommended for the peak and off-peak hours period. A GPS-based toll collection is only applicable on the Motorway for peak hours, and a GPS-based toll collection is applied in both road options off-peak hours. This strategy in a 24-hour span would allow a total decrease in emissions-related externalities (-1.4%) with only a small decrease of the total revenue without sacrificing the cost each user would pay to travel through this intercity corridor. Bearing in mind the residual emission reductions and the level on uncertainty associated with the model, these results are promising in that they suggest that it is plausible to implement a system that internalizes emission costs more directly as a function of demand, driving conditions and speed.publishe

Could CAVs be future eco-driving agents to influence the environmental performance of road traffic?

Connected Autonomous vehicle (CAVs) could be an environmental boon or disaster, depending on public policy [1]. At operational level, CAV technologies are expected to improve fuel economy and reduce emissions per unit of distance thanks to more gradual acceleration and deceleration patterns [2] and fewer stop-and-go movements [3]. Under a likely transitional stage of co-existence of connected and automated vehicles (CAVs) and conventional vehicles (CVs), this study explores the potential effects of CAVs to reduce greenhouse gases (GHG) and pollutant emissions in different road types based on improved operational parameters. For that purpose, CAVs were assumed to behave as eco-driving agents to influence the environmental performance of overall traffic. A microscopic traffic and emission model platform was applied to simulate a European medium-sized city during the morning peak period. Three roadway sections, including motorway, rural and urban, were selected to evaluate in detail the impact of CAVs in different roads types and over multiple CAVs penetration rates (MPR) to address the following questions: 1) What is the potential reduction of carbon dioxide (CO2) and nitrogen oxides (NOX) emissions resulting from CAVs operating in different road typologies? 2) How can network-wide emissions and fuel consumption vary according to different MPR of CAVs? 3) May CAVs significantly influence the energetic and environmental performance of CVs on different road types? Results allow assessing the main research questions defined, concretely: 1) CAVs impacts were particularly beneficial for the environment in the road segment “national road”, with emission reductions up to 12%. In the urban corridor, the impacts were shown to be detrimental due to an inefficient configuration of the car following adjustment parameters (CFAP) in the local context and a slight increase in the capacity of the upstream intersections. At the motorway level operating at low volume-to-capacity (V/C) ratio, impacts are negligible. Nevertheless, an optimization of the speed to 90 km/h allows reductions up to 18% of CO2 and 32% of NOx. 2) In sections outside the urban context, the environmental impacts resulting from the presence of CAVs are positive, following a strong linear relationship and in line with higher MPR. 3) CAVs showed to significantly influence the energetic and environmental performance of CVs ranging from 3 to 13%. These results suggest that even CAVs will be predominantly fully electric in the near future, the impact on network-wide emissions should be taken into account and adjusted to different driving scenarios.publishe

Assessing the emission impacts of autonomous vehicles in metropolitan freeways

Road transport was in 2016 responsible for 74% of the 33% energy consumed by the transport sector [1]. Passenger cars accounted for 41% of the transport greenhouse gases (GHG) in the European Union countries, in 2016 [2]. Intelligent Transport Systems (ITS) have been supporting the autonomous vehicles (AVs) technology, that offer numerous benefits such as to allow for more productive use of time spent in vehicles, reduce energy use and land use [3]. AVs have shown promising results at both social and economic levels but there is no consensus about their environmental benefits in a context of traffic context. Thus, this research assessed the environmental and traffic performance impacts of the AVs in an urban freeway corridor in a metropolitan area. The proposed methodology resorted to VISSIM tool to code and assess traffic operations [5]. Vehicular emissions were estimated using the Vehicle Specific Power (VSP) and EMEP/EEA methodologies [6;7]. The case study is based in the city of Porto (Portugal), which according to data from 2016 from TomTom, was the second city in the National ranking where drivers spent more time in traffic (~31 minutes) [8]. The candidate freeway is a stretch (~9km in length) of the Via de Cintura Interna (VCI), 8 interchanges, 3-4 lanes by travelling direction, 80 km/h speed limit and an average daily traffic ranged from 113 680 to 149 520 [9]. Three different AV penetration rates based on long-term market prediction (10%,20% and 30%) for through traffic along VCI were implemented [10]. These scenarios were compared in terms of emissions (carbon dioxide, carbon monoxide, nitrogen oxides and hydrocarbons) and traffic performance (travel time and stop-and-go situations) against current situation – conventional vehicles (CVs) only. Emissions and traffic performance scenarios were assessed on three levels: 1) overall study domain; 2) corridor; and 3) impact of AVs on CVs. Results confirmed that impacts of AVs were not statistically significant for penetration rates below 30% in the overall study domain (reductions up to 2% for pollutants emissions and average travel time). Corridor-level analysis showed that a decrease of 5% on emissions can be expected with AVs technology, but travel time is penalized up to 13% for both AVs and CVs, comparing to the actual situation. Furthermore, results showed that the increase of AV rates may result in higher travel times for CV, although stop-and-go situations decreased. In summary, this research sought to contribute for better understanding of future AV penetration rates scenarios for both traffic performance and carbon dioxide, carbon monoxide, nitrogen oxides and hydrocarbon emissions on congested freeways. Also, it provides solid knowledge of the differences in traffic-related impacts between AV and CV, and of the incorporation of eco-routing algorithms to govern AV operations.publishe

Real-time prevention tool integrating volatility and environmental impacts

In Europe, the number of road traffic deaths and injuries is still far too high and the European Union is committed in improving road safety and move closer to the target of approaching zero road fatalities by 2050. For that purpose, new strategies based on the Safe System approach to preventing deaths and serious injuries for all road users should be developed. Road transport is a major source of pollutant emissions. In particular, it is responsible for the emission of harmful pollutants such as nitrogen oxides (NOx) and carbon dioxide (CO2), which has serious impacts in global warming [1]. It is known that driver behavior can play a key role in what concerns road crashes and pollutant emissions. Such impacts increase when associated to aggressive behavior, experiencing high and extreme levels of fuel consumption, speed and acceleration. A deep understanding of driver behavior should be an important step to improve road safety. Various studies have been conducted to identify driver’s behavior under many contexts such as, traffic, roadway and weather conditions. An issue that has not been so explored is an analysis of drivers’ volatility [2-3]. Volatility can be defined as the extent of variations in driving, which can be characterized by accelerations/braking, lane change and also unusual high speed for roadways conditions. Therefore, particular attention should be given to developing preventive tools, anticipating dangerous situations and warning the driver that may be efficient solutions to avoid an occurrence. In [4], an advisory system was developed on a driver’s simulator to warning the driver. However, there is no preventive tool in the literature that integrates volatility and environmental impacts. The main objective of this work is to develop a decision support system to evaluate driver volatility and provide instantaneous and integrated information on safety and emission impacts to the driver. To validate our application, we used real traffic, dynamic and on-road emissions data collected from probe vehicles on two highways of different specificities (e.g., slope, relief and traffic volumes). A simulation-based approach through Vissim COM API using Matlab was constructed in order to give to the driver warnings regarding safety and emissions. Markov Decision Process (MDP) was used to support the decision on safety and the Vehicle Specific Power (VSP) methodology was used for estimating pollutant emissions.publishe

Influência do tempo de viagem nos custos ambientais

Em cidades de média dimensão com baixa densidade populacional, o sistema de transporte públicos tende a ser deficitário, sendo, portanto, a utilização do veículo rodoviário individual o modo de transporte predominante. A utilização deste meio de transporte pode levar a congestionamento a certas horas do dia, implicando um aumento das emissões. Neste trabalho propôs-se estudar a influência que o congestionamento tem nas emissões e nos custos ambientais. Com este objetivo, num caso de estudo, foi estimado o aumento de emissões/custos que um cenário com congestionamento tem em relação a um cenário sem congestionamento. Foi verificada a emissão de poluentes para um veículo típico a diesel e um a gasolina, e para um cenário de congestionamento as emissões de CO2 aumentaram 32% e 46%, e os custos ambientais 33% e 46%, respetivamente para cada tipo de veículo. Neste estudo foi também possível desenvolver modelos que descrevem os custos ambientais em função do tempo de viagem e da distância percorrida. Foi estimado o custo para as 3 rotas sugeridas para realizar uma certa viagem; conclui-se que uma das rotas implica menores custos ambientais, cerca de 20% menos do que a rota que implica maiores custos ambientaispublishe

Multiobjective bilevel equilibrium model for traffic assignment

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Exploring the potential of web based information of business popularity for supporting sustainable traffic management

This paper explores the potential of using crowdsourcing tools, namely Google "Popular times" (GPT) as an alternative source of information to predict traffic-related impacts. Using linear regression models, we examined the relationships between GPT and traffic volumes, travel times, pollutant emissions and noise of different areas in different periods. Different data sets were collected: i) crowdsourcing information from Google Maps; ii) traffic dynamics with the use of a probe car equipped with a Global Navigation Satellite System data logger; and iii) traffic volumes. The emissions estimation was based on the Vehicle Specific Power methodology, while noise estimations were conducted with the use of “The Common Noise Assessment Methods in Europe” (CNOSSOS-EU) model. This study shows encouraging results, as it was possible to establish clear relationships between GPT and traffic and environmental performance.in publicatio

Information Management for Smart and Sustainable Mobility

The main objective of this work is to present a modular platform to manage traffic information for smart mobility. The management and collection of dynamic data is a challenging process especially in the context of low penetration of floating car data (FCD) and limited availability of traffic monitoring stations. In this work, three different road segments of a European medium-sized city were selected to collect vehicle dynamic data over multiple scenarios of traffic demand. Simultaneously, traffic volumes were recorded in real time. The main objective of this pilot experiment was to assess how it would be possible to read and predict traffic congestion and emissions levels with limited information and how data from multiple sources should be managed in order to correlate and deal with this information in real time. It was possible to correlate simultaneously multiple data set such as congestion values, specific vehicle power (VSP) mode distribution, Google traffic data and emission. Preliminary findings suggest that in urban arterials travel time and congestion levels can be reliable indicators for estimating emissions in real time. In sections of rural arterials, the estimation of real-time traffic performance is more complex. Key issues towards the implementation of a prototyping platform in an urban context are also discusse