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How can urban policies and planning approaches help in achieving a safer mobility and carbon reduction in the transport sector? The attention of planners and policy makers towards the promotion of sustainability and reduction of environmental impacts has grown in recent years. This paper investigates the role that Urban Planning plays in the long term towards a safer and climate friendlier mobility, highlighting the need for integrated approaches gathering spatial planning and mobility management. After a review of several urban policies and planning strategies, initiatives, and approaches, mainly based on the urban scale, the paper presents an urban regeneration case study leading to an increase of pedestrian accessibility at the neighborhood level. This can be seen as a support tool to foster sustainable, safe, and climate friendly mobility in cities. The results of the performed analysis show a dependency of accessibility from two different factors: the distribution of services and the capillarity of the soft mobility network, which can contribute to creating a more walkable space.

Stone pavements are the historical, architectural, and cultural heritage of lots of cities in Italy and the world. Road managers should be able to make decisions on the global conditions to define the most suitable strategies and maintenance interventions for every type of pavement. There are no standard monitoring methods or criteria for evaluating stone pavement performance. These pavements have more uneven surfaces than traditional pavements, but this characteristic could be accepted if type of vehicles and relative travel conditions are considered. Therefore, it is useful to define criteria for assessing roughness considering the comfort experienced by users in different vehicles. In this research, both traditional and innovative methodologies for assessing irregularities have been investigated using true stone surface profiles. In this regard, traditional performance indicators such as the International Roughness Index (IRI) defined by the ASTM E1926, the ISO 8608 classification, and the frequency-weighted vertical acceleration (awz) provided by ISO 2631-1 for comfort assessment have been considered. In the case of comfort assessment, three dynamic vehicle models (bike, automobile, and bus) have been adopted. Finally, this two-part paper also proposes an innovative straightedge analysis for stone pavements (SASP) to evaluate the effect on traffic of both pavement profile roughness and localized irregularities. In this way, the authors aim to provide an effective tool to monitor stone pavements.

In the context of global suburbanization and population aging, a low-speed, automated vehicle (LSAV) system provides essential mobility services in suburban residential areas. Although extensive studies on shared autonomous vehicle (SAV) services have been conducted, quantitative investigations on the operation of suburban LSAV systems are limited. Based on a demonstration pilot project of an autonomous vehicle called “Slocal Automated Driving”, we investigated the performance of an SAV system considering several scenarios in Kozoji Newtown, a suburban commuter town in Japan. The agent-based simulation results revealed that 40 LSAVs can satisfy the demands of 2263 daily trips with an average wait time of 15 min. However, in the case of a high-speed scenario, the same fleet size improved the level of service (LOS) by reducing the average wait time to two and a half minutes and halving the in-vehicle time. By contrast, the wait time in terms of the average and 95th percentile of the no-sharing ride scenario drastically deteriorated to an unacceptable level. Based on the fluctuations of hourly share rates, wait times, and the number of vacant vehicles, we determined that preparing for the potential fleet insufficiency periods from 7:00–13:00 and 15:00–18:00 can improve the LOS.

The main interest of this paper is to analyze the mobility acceptance factors of an automated shuttle bus last-mile service. There is limited research on the passengers’ perception of security and safety of automated mobility, whereas prior research is mostly based on surveys interested in attitudes towards self-driving vehicles, without being linked to the experience. We, on the other hand, are interested in passengers’ feeling of security and safety, after taking a ride with an automated shuttle in an open urban environment. For studying this, we conducted an automated shuttle bus last-mile pilot during a four-month period in the city of Tallinn in late 2019. The method is a case study focusing on one city with several tools for data collection applied (surveys, interviews, document analysis). The pilot, open and free for everybody, attracted approximately 4000 passengers, out of which 4% responded to the online feedback survey. For studying the operational capacity, we had a panel interview with operators of the shuttle service, in addition to analyzing daily operational log files. The results indicate that passengers’ perceived feeling of security and safety onboard was remarkably high, after taking a ride (and lower without a ride, in a different control group). The bus was operated only if operational capacity was secured, thus having significant downtime in service due to environment, technology and traffic-related factors.

Understanding the nature of consumers’ environmental behaviors will help design better environmental policies for a sustainable future. Drawing on the responsible environmental behavior (REB) theoretical framework, we disentangle the effects of social and psychological environmental factors on Europeans’ behaviors, considering that living contexts vary from country to country. Using data on attitudes to the environment sourced from the 2017 Eurobarometer, we measure the socio-psychological factors and environmental behaviors using exploratory factor analysis. A multilevel model measures the effect of individual-level environmental factors and analyzes the impact of the country context on Europeans’ environmental behaviors. Results show that the three tested environmental behaviors (eco-friendly purchasing, public transport use, and reduced resource consumption) are explained by individual-level environmental factors as well as by country differences, but the effects differ depending on the behavior considered. We also find that the effects of knowledge, attitudes, and perceived behavioral control are mediated by a set of social indicators (age, gender, education, and income). We conclude with a discussion of the implications for policymakers.

Transport in cities is one of the most important sources of emissions. Electromobility is an essential element in the catalogue of activities of local authorities aimed at combating climate change. Over the years trolleybus transport has been characterised by both phases of development and regression and is still an essential component of zero-emission urban transport in about 300 cities worldwide. The development of electricity storage technology, especially in the form of a battery, has opened up new prospects for this mode of transport. A trolleybus equipped with a battery (in-motion charging technology) gains unique characteristics for operation independent of the catenary. This study presents the approach for assessing the development of in-motion charging for trolleybuses in all Polish cities operating this means of transport. A set of KPIs has therefore, been set and analysed. The analysis covers a comparison between 2014 and 2019, aimed at showing the development of technological innovations in this field. The results clearly show that in-motion charging technology leads to the development of trolleybus transport, although this development has mainly a qualitative dimension. A key factor determining the development of trolleybus transport using in-motion charging technology is progress in the development of traction batteries.

Public transport systems can be characterised by a schedule-based or a frequency-based framework according to the kind of service to be operated. In the former case, specific departure and arrival times are set for each run and disclosed to the users; in the latter, instead, it is necessary to maintain a certain headway between two successive runs, rather than a specific timetable structure. This paper focuses on modelling frequency-based systems, which can be described by means of the so-called Merry-Go-Round (MGR) paradigm. The paradigm is first discussed and the related analytical formulation is presented; the role of the terminal station layout is then investigated within this framework. Finally, in order to show the effectiveness of the proposed formulation, it was implemented in the case of a real-scale metro line.

Measuring the traffic quality and congestion level is fundamental in highway engineering, and several decades of studies and research have pursued this specific objective, especially for freeways. Nowadays, smart technologies on personal devices and information shared by users have made available various online information platforms that provide dynamic representations of the use of the road network. If, on the one hand, these tools provide a simple and direct representation of the quality of circulation, on the other hand, their aggregated information is only partial for those dealing with traffic and highway engineering. This branch of engineering relies on multidimensional knowledge of traffic flow phenomena, and only through their in-depth knowledge, we can assess traffic quality and congestion risk. After identifying the different approaches for analyzing in quantitative terms the traffic quality on the freeway, the paper deepens the reliability approach. From this point of view, the paper aims to unite the two perspectives in the literature, namely, the probabilistic analysis of traffic instability with the characterization of speed random processes and the analysis of breakdowns with the survival analysis. For this purpose, the work outlines a procedure based on the estimation and simulation of ARIMA models for speed random processes in a freeway section, particularly on the leftmost lane, to assess the traffic reliability function. Applying the Product Limit Method to the Monte Carlo simulation results makes it possible to obtain probabilistic assessments of congestion, considering the Level of Service density limits defined in the Highway Capacity Manual. Its application to a case study makes it possible to illustrate the application of the method, which can be easily applied to historical and near-real-time data using a continuous flow of information.