Assessing the Impacts of Crowdshipping Using Public Transport: A Case Study in a Middle-Sized Greek City

Crowdsourced deliveries or crowdshipping is identified in recent literature as an emerging urban freight transport solution, aiming at reducing delivery costs, congestion, and environmental impacts. By leveraging the pervasive use of mobile technology, crowdshipping is an emerging solution of the sharing economy in the transport domain, as parcels are delivered by commuters rather than corporations. The objective of this research is to evaluate the impacts of crowdshipping through alternative scenarios that consider various levels of demand and adoption by public transport users who act as crowdshippers, based on a case study example in the city of Volos, Greece. This is achieved through the establishment of a tailored evaluation framework and a city-scale urban freight traffic microsimulation model. Results show that crowdshipping has the potential to mitigate last-mile delivery impacts and effectively contribute to improving the system’s performance.

Digitalization and Automation in Intermodal Freight Transport and Their Potential Application for Low-Income Countries

This paper presents an assessment of enabling technologies in intermodal freight transport. It first identifies the technologies used in intermodal freight transport globally using a systematic literature review. Then, it characterizes intermodal freight transport in the context of low-income countries to assess the potential application of digitalization and automation for the countries. Countries with a per capita gross national income (GNI) lower than $1025 are categorized as low-income countries. To achieve the objectives, a review was undertaken of 147 published articles from Scopus, Web of Science, and Transport Research International Documentation (TRID). Furthermore, distinctions of intermodal transport in low-income countries were also characterized using gray literature. A number of enabling technologies applied at components of intermodal transport were identified. The results demonstrated that several enabling technologies such as wireless communication technology, sensors, positioning technology, and web-based platforms are highly utilized in intermodal freight transport globally. In contrast, electronic data interchange (EDI), wireless communication technologies, and web-based platforms also have potential applications in low-income countries, and their adoption should be studied further.

Investigating the Impact of Connected and Automated Vehicles on Signalized and Unsignalized Intersections Safety in Mixed Traffic

Road traffic crashes are a major safety problem, with one of the leading factors in crashes being human error. Automated and connected vehicles (CAVs) that are equipped with Advanced Driver Assistance Systems (ADAS) are expected to reduce human error. In this paper, the Simulation of Urban MObility (SUMO) traffic simulator is used to investigate how CAVs impact road safety. In order to define the longitudinal behavior of Human Drive Vehicles (HDVs) and CAVs, car-following models, including the Krauss, the Intelligent Driver Model (IDM), and Cooperative Adaptive Cruise Control (CACC) car-following models were used to simulate CAVs. Surrogate safety measures were utilized to analyze CAVs’ safety impact using time-to-collision. Two case studies were evaluated: a signalized grid network that included nine intersections, and a second network consisting of an unsignalized intersection. The results demonstrate that CAVs could potentially reduce the number of conflicts based on each of the car following model simulations and the two case studies. A secondary finding of the research identified additional safety benefits of vehicles equipped with collision avoidance control, through the reduction in rear-end conflicts observed for the CACC car-following model.

Key Elements of Mobility Apps for Improving Urban Travel Patterns: A Literature Review

In recent decades cities have applied a number of policy measures aimed at reducing car use and increasing public transportation (PT) patronage. Persuasive strategies to change mobility behavior present notable limitations in economic and logistical terms and have only minor impacts. The smartphone has emerged as a promising tool to overcome these challenges, as it can host persuasion strategies through mobility apps. Simultaneously, Mobility-as-a-Service (MaaS) schemes could open up new possibilities for addressing both sustainability goals and the needs of urban travelers. This paper carries out a literature review to identify the key elements of mobility apps that foster more sustainable travelers’ choices. The findings show that some persuasive strategies such as eco-feedback, rewards or social challenges are effective because they are well received by users. From the users’ point of view, the perceived barriers (e.g., usability, privacy) relate negatively to app adoption, and it is considered useful to include functional needs such as real-time information (e.g., to avoid congestion), cost savings (e.g., customized multimodal packages), comfort (e.g., crowding on public transport) or health (e.g., calories burned). We have found that a proper design of multimodal travel packages based on (i) financial incentives and (ii) environmental awareness, could help increase public transport patronage and reduce private car use.

Identifying Public Perceptions toward Emerging Transportation Trends through Social Media-Based Interactions

The objective of this study is to mine and analyze large-scale social media data (rich spatio-temporal data unlike traditional surveys) and develop comparative infographics of emerging transportation trends and mobility indicators by adopting natural language processing and data-driven techniques. As such, first, around 13 million tweets for about 20 days (16 December 2019–4 January 2020) from North America were collected, and tweets closely aligned with emerging transportation and mobility trends (such as shared mobility, vehicle technology, built environment, user fees, telecommuting, and e-commerce) were identified. Data analytics captured spatio-temporal differences in social media user interactions and concerns about such trends, as well as topics of discussions formed through such interactions. California, Florida, Georgia, Illinois, New York are among the highly visible cities discussing such trends. Being positive overall, people carried more positive views on shared mobility, vehicle technology, telecommuting, and e-commerce, while being more negative on user fees, and the built environment. Ride-hailing, fuel efficiency, trip navigation, daily as well as shopping and recreational activities, gas price, tax, and product delivery were among the emergent topics. The social media data-driven framework would allow real-time monitoring of transportation trends by agencies, researchers, and professionals.

Sustainable Mobility Issues of Physically Active University Students: The Case of Serres, Greece

The mobility practices of students are largely dictated by their respective educational obligations. Students of physical education are an ostensibly physically active population, whose active lifestyle may include active travel. University student mobility research calls for behavioral approaches to ground relevant interventions. This work investigated the sustainability practices in the student community of the Physical Education Department in Serres, a medium-sized Greek city. Moreover, this paper aimed to shed light on the gender differences in the physical activity levels of 259 students, as well as their respective differences in mobility practices. A novel questionnaire, based on Ajzen’s theory of planned behavior and Godin–Shephard’s approach to physical activity, was used. The results confirmed higher levels of physical activity in male students, although their attitude toward physical activity was less positive than that of their female classmates. Further positive attitudes in women were recorded toward sustainable mobility choices, although the evidence demonstrated a similar gap between the answers of the two genders. Car possession was higher in men, whereas car purchase intention was slightly lower in women, who had a lower income in general. Moreover, income impacted gender mobility preferences. Recommendations can be guided by students’ sports preferences and can be gender-sensitive, taking income into account.

A Hybrid LSTM-CPS Approach for Long-Term Prediction of Train Delays in Multivariate Time Series

In many big cities, train delays are among the most complained-about events by the public. Although various models have been proposed for train delay prediction, prior studies on both primary and secondary train delay prediction are limited in number. Recent advances in deep learning approaches and increasing availability of various data sources has created new opportunities for more efficient and accurate train delay prediction. In this study, we propose a hybrid deep learning solution by integrating long short-term memory (LSTM) and Critical Point Search (CPS). LSTM deals with long-term prediction tasks of trains’ running time and dwell time, while CPS uses predicted values with a nominal timetable to identify primary and secondary delays based on the delay causes, run-time delay, and dwell time delay. To validate the model and analyse its performance, we compare the standard LSTM with the proposed hybrid model. The results demonstrate that new variants outperform the standard LSTM, based on predicting time steps of dwell time feature. The experiment results also showed many irregularities of historical trends, which draws attention for further research.

Scenarios of Automated Mobility in Austria: Implications for Future Transport Policy

Developments in the field of automated mobility will greatly change our mobility and the possibilities to get from one place to another. This paper presents different scenarios for personal mobility in Austria, anticipating the possibilities and developments in the field of automated vehicles (AVs). The scenarios were developed using a systematically formalized scenario technique and expand the social and political discourse on automated mobility, which is currently characterized by a lack of experience and visibility as an established transport service. Using system dynamics modeling techniques, i.e., the Metropolitan Activity Relocation Simulator (MARS), impacts of the scenarios on the Austrian transportation system are estimated. The simulations show that, without suitable transport policy measures, automated mobility will lead to a significant increase in the volume of individual traffic and to modal shift effects with lower traffic volumes for public transport, walking and cycling. In addition, without a link between AVs and post-fossil propulsion systems, increases in pollutant emissions can also be expected. In contrast, the simulation results of an increased use of AVs in public transport show positive effects for the support of a more sustainable mobility. Hence, transport policy measures accompanying the introduction and development of automated vehicles will be needed in the future to reach a sustainable development.

Estimating the Potential of Electric Vehicles for Travelling to Work and Education in Melbourne, Victoria

This paper aims to estimate the potential of electric vehicles (EVs) in Melbourne, Victoria, using the Victorian Integrated Survey of Travel and Activity (VISTA) data. The investigation of whether EVs with different all-electric ranges (AERs) can replace car travel to work and education is the focus of this paper. The results showed that EVs would be able to replace most car travel to work (68.5% to 97.1%) and car travel to education (71.9% to 96.9%), with AERs increasing from 40 km to 100 km, assuming car drivers are willing to use an EV. It is estimated that the average operating cost savings per person would be up to AUD 3.12 and AUD 2.79 each day, regarding travel to work and education, respectively. Considering both travel to work and education, EVs could replace up to 33.8 million kilometres of car travel, consuming around 7.6 GWh and resulting in a reduction in carbon dioxide (CO2) emissions of about 610 tons each day.

Leveraging LiDAR Intensity to Evaluate Roadway Pavement Markings

The United States has over 8.8 million lane miles nationwide, which require regular maintenance and evaluations of sign retroreflectivity, pavement markings, and other pavement information. Pavement markings convey crucial information to drivers as well as connected and autonomous vehicles for lane delineations. Current means of evaluation are by human inspection or semi-automated dedicated vehicles, which often capture one to two pavement lines at a time. Mobile LiDAR is also frequently used by agencies to map signs and infrastructure as well as assess pavement conditions and drainage profiles. This paper presents a case study where over 70 miles of US-52 and US-41 in Indiana were assessed, utilizing both a mobile retroreflectometer and a LiDAR mobile mapping system. Comparing the intensity data from LiDAR data and the retroreflective readings, there was a linear correlation for right edge pavement markings with an R2 of 0.87 and for the center skip line a linear correlation with an R2 of 0.63. The p-values were 0.000 and 0.000, respectively. Although there are no published standards for using LiDAR to evaluate pavement marking retroreflectivity, these results suggest that mobile LiDAR is a viable tool for network level monitoring of retroreflectivity.