Future Transp., Volume 2, Issue 2 (June 2022) – 14 articles

This study evaluated the effects of COVID-19 on attitudes toward residential associated with travel behavior on decisions regarding future relocation. Chi-square automatic interaction detection was used to generate tree and classification segments to investigate the various segmentations of travelers and residents around mass transit stations. The decision tree revealed that the most influential variables were the number of transport card ownerships, walking distance to the nearest mass station, number of households, type of resident, property ownership, travel cost, and trip frequency. During the COVID-19 pandemic, people have concentrated on reducing travel time, reducing the number of transfers, and decreasing unnecessary trips. Consequently, people who live near mass transit stations less than 400 and 400–1000 m away prefer to live in residential and rural areas in the future. Structural Equation Modeling was used to confirm the relationship between attitudes in normal and pandemic situations. According to the findings, attitudes toward residential accessibility of travel modes were a significant determinant of attitudes toward residential location areas. This research demonstrates travelers’ and residents’ uncertain decision-making regarding relocation, allowing policymakers and transport authorities to better understand their behavior to improve transportation services. Full article

How safe will Automated Vehicles (AVs) be? It depends on technologies, vehicles, other road users, and infrastructures. The only certainty is that automated vehicles will not solve all road safety issues. As the penetration of AVs might be much more sudden than road infrastructure modification, the infrastructure revision process should start immediately by then, and road authorities must be ready in turn. This paper presents an approach based on road safety inspections to quantify and compare the expected safety levels for a road section with different combinations of Automated and Conventional Vehicles (CVs). Developed in the framework of the EC project CoEXist, the proposed approach allows for identifying the possible safety issues that may arise when introducing AVs. All safety issues are judged in terms of risk, which is the product of the exposure, the likelihood of the crash, and the severity of the possible consequences. All these aspects are analyzed in different scenarios considering different AV penetration levels and driving logic. The results show a potential risk decrease according to the higher penetration of AVs. Nevertheless, the analysis highlights that safety issues may arise with the introduction of specific AVs’ driving logics. Full article

In the past decade, as more and more passengers choose to fly on trips, China’s airport infrastructure construction has achieved world-renowned achievements. Despite the growing opportunities and demands for using brand research to assist airport industry services in improving, few studies have investigated the impact of service quality in terminal facilities on brand due to the diversity of service. This study uses structural equation models based on empirical research to explore the impact of facility service quality, including processing facility and non-processing facility, on airport brand experience and passenger satisfaction. This study also aims to assess the mediating effect of brand engagement on the relationship between facility service quality, brand experience, and passenger satisfaction. At the same time, this study also uses importance–performance map analysis (IPMA) to find specific items influencing brand engagement. The sampling method used a random sampling approach, with a total of 186 questionnaires distributed at Shanghai Pudong International Airport for data analysis. The results show that airport facility service quality is significant for brand engagement and experience, as well as for satisfaction, especially for processing facilities. In addition, the IPMA results show that facility services involved in the check-in process is of high importance, which requires more attention from managers. Overall, the findings of this study extend the understanding of service quality, brand engagement, brand experience, and passenger satisfaction in the context of an international airport, and they offer implications for Shanghai Pudong International Airport regarding the improvement of its facilities and brand. Full article

Improperly scheduled signal timing plans are one of the main reasons for reduced efficiency of traffic signals at coordinated urban arterials. Recently, most urban arterial roads are equipped with intelligent transportation systems devices capable of reporting the collected data on high temporal and spatial resolution, which gives us the opportunity to overcome traditional signal timing planning flaws. Previous studies have proposed methods for scheduling signal timing plans based on small quantities of data combined with various optimization approaches that ultimately require domain expert intervention to fine-tune proposed solutions. Consequently, the signal timing plans scheduling problem is still being addressed without a comprehensive approach. In this study, we propose a novel data-driven procedure based on visual analytics principles to identify the dominant traffic profiles and appropriate scheduling of signal timing plans. The medium-resolution volume data collected over a one-year period on a real-world corridor consisting of 12 signalized intersections were used to validate the proposed methodology. Applied principles from the visual analytics domain allow for a better understanding of traffic characteristics and ultimately alleviate the development of appropriate signal timing schedules. The results show that the proposed method more reliably schedules signal timing plans when compared to current practice. Full article

The rapid rise of e-commerce and advances in the technological sector have enabled the emergence of modern business models on e-marketplaces. Digital and on-demand e-marketplaces have become increasingly popular, responding to the trend of the sharing economy in various business sectors, but there is little guidance on how to develop the respective business models in order to ensure the long-term sustainability of such companies. The present paper is based upon the development and operation of an innovative on-demand warehousing e-marketplace in Greece as a one stop-shop for on-demand warehousing services. The purpose of this paper is to identify the necessary components for developing a successful innovative business model for a viable and effective on-demand warehousing platform. The identification of the value proposition of the proposed e-marketplace, the necessary business infrastructure and the customer interface are described, analyzed and adapted to the Greek Market. Furthermore, this paper also describes the cost structure and the revenue streams of the proposed on-demand warehousing e-marketplace. Full article

The various social, political, environmental, and economical mega-trends together with the rapid rise of digitalization are shaping cities worldwide including the way that citizens and products move within the urban environment. New mobility services are popping up which are showing the path toward more on-demand and personalized mobility patterns. The transformation of the urban mobility environment though constitutes a great challenge for policy-makers, as little is known about the impact of these trends on the city’s current mobility environment while the assessment of the future state of a mobility system can be considered a quite complex process taken its multi-parametric and multi-dimensional nature. This paper aims to simplify this process by presenting a coherent multi-actor consequence analysis framework as a tool for assessing future mobility scenarios which was developed in the framework of the H2020 SPROUT. This paper presents in detail the main components of this framework together with the list of key performance indicators used for assessing the performance in terms of sustainability of future mobility scenarios as well as indicative results from its use by the city of Padua. Full article

To support worker and driver safety, this study conducted a comprehensive literature review to identify methods of enhancing TMA visibility, improving work zone configurations, and ensuring worker safety. To increase TMA recognition, this study observed that the use of a 6-to-8-inch wide yellow and black inverted ‘V’ pattern of retroreflective chevron markings, sloped at a 45-degree angle downward in both directions from the upper center of a rear panel is effective in alerting drivers to work zones. This study also recommends applying amber and white warning LEDs, which flash in an asynchronous pattern at a 1 Hz frequency and are mounted against a solid-colored background for a 360-degree view visible at least 1500 feet from the work zone. In addition, a work zone vehicle configuration consisting of a lead, buffer, and advance warning truck with a buffer space between 100 and 150 ft is suggested to reduce the risk of lateral intrusions and TMA roll-ahead. In parallel, workers should wear high-visibility vests noticeable at a minimum distance of 1000 feet and headwear with at least 10 square inches of retroreflective material. Some intelligent transport systems are also suggested to enhance TMA recognition and potentially minimize work zone fatalities. Application of the recommended guidelines will potentially improve current practices and significantly reduce the occurrence of TMA crashes in construction and maintenance work zones. Full article

In this paper we assess the transit quality of service (QoS) from a user’s standpoint, using smart data. A number of bus lines with different characteristics, operating in the Metropolitan Area of Athens, were chosen as a case study. The data used were gathered by an Automatic Passenger Counting (APC) system. APC technologies provide exact temporalized passenger counting along the line for each service, thus assisting to better understand causalities of delays and avoid operational problems. By employing archived APC data from buses running on crosstown routes between 15 January 2019 and 15 April 2019 we conducted a statistical analysis to explore occupancies and assess QoS, including under a social distancing scenario. The passenger distribution along the stops, the bus’s occupancy level, the stops that are maximum occupancy points and their rate of occurrence and, lastly, the passenger’s average trip length during the day and the week are examined. Full article

Wyoming has one of the highest fatality rates and lowest enforcement rates in the U.S. Thus, this study was conducted to see if there is any link between various citation types on the equivalent property damage only (EPDO) crashes, while taking into account interaction terms between various crashes and citation characteristics. To achieve the objective, we disaggregated the citations data into their related types, while aggregating the crash dataset across each segment through the Eastern direction of the interstate 80 in Wyoming. The results highlighted that there are important relations between types of crashes and related citations. For instance, it was found that there are (1) significant interaction terms between driving-too-fast-for-condition citations and underride crashes, (2) total number of commercial citations and commercial motor vehicle (CMV) crashes, and (3) between seatbelt citations and seatbelt-related crashes, highlighting, possibly, that the performance of the highway patrol was adjusted based on the specific roadway concerns, or there is indeed a significant link between related citation and crash types. While conducting the model with no interaction term, the results of comparison highlighted that the model erroneously highlight positive associations between citation types and the EPDO crashes. This is one of the earliest studies that considered disaggregated citation data and aggregated them with the crash data to highlight the important relationship between the citations and crashes. Full article

Heavy-duty trucks (HDTs) are responsible for considerable fuel consumption and greenhouse gas emissions (GHG) in the road transportation sector due to their heavier weight, and significantly more miles travelled in comparison with other vehicles. Regarding the climate change mitigation policies, HDTs need to become zero-emission vehicles. One of the technological solutions in this sector is the battery electric truck (BET). This paper includes a systematic review on relevant studies in the field of BETs, including the following: (1) the technical, stakeholder, and customer aspects in terms of charging solutions to give a comprehensive insight into their technological advantages and disadvantages; (2) the total cost of ownership (TCO) for BETs and diesel trucks; and (3) a CO₂ life cycle assessment (LCA) from different technologies. Moreover, the result is formulated in the form of SWOT analysis to describe the strengths, weaknesses, opportunities, and threats of different charging technologies. Moreover, the different calculation methods of the total cost of ownership for the heavy-duty battery trucks and diesel trucks are compared. In addition, the CO₂ LCA is analyzed, and the different estimation methods of the CO₂ released in the environment are compared, which includes the CO₂ emissions during mobility operations and during the different manufacturing processes. Full article

This study examines the relationship between travel modes and the attitudes of residents and travelers around mass transit stations. The importance of this study was emphasized by considering that the attitudes toward residence could affect future travel and relocation considerations. In particular, the outbreak of COVID-19 may have a significant effect on their relationship. To investigate the direct and indirect effects before and during the COVID-19 pandemic, a moderated mediation model was used to test the hypothesis of this study by three-step approach analysis. The attitude toward residence was defined to test the hypothesis of the mediator, and the walking distance to the nearest mass transit station was employed to identify the level of the moderator. The results indicated that the attitude toward residence mediated the relationship between the attitude toward travel mode and travel mode behavior. The sensitivity of COVID-19 accurately reflects the various effects on travel mode. Moreover, multi-group analyses show that walking distance moderators have a direct effect on attitudes toward travel mode and travel mode behavior as well as the attitude toward residence. Full article

Over the last several decades, transportation scientists have made substantial progress in identifying and tackling transport-related problems by elaborating sophisticated policy instruments. Originally, the policy instruments were developed and designed to tackle inefficiencies caused by conventional (human-driven) vehicles. However, questions remain regarding transportation policies, especially pricing instruments, in the future. With the advent of fully autonomous vehicles (driverless or self-driving cars), many of potentially disruptive changes to our transportation system are projected to occur. This gives rise to the question of how to adapt the existing, well established, policy instruments to make them applicable to a world of self-driving cars. The present paper utilizes one of the most widely deployed, most important (in terms of tax revenue), and most effective (in terms of carbon dioxide mitigation) current price-based policy instruments in the transport sector (i.e., the energy tax) to show how one of the most innovative features associated with fully autonomous vehicles (i.e., driverless vehicle relocation) affects the optimal design of a transportation tax. We adopt a microeconomics optimization framework and analytically derive the optimal energy tax under the assumption that driverless vehicle relocation is possible. Our main finding is that in a world of self-driving cars, the energy tax (likewise, a second-best miles tax) as a price-based policy instrument becomes more difficult to evaluate. With the capability of fully autonomous vehicles to relocate without passengers inside, the (analytical) expression for the optimal energy tax becomes more complex, and its (numerical) determination becomes more difficult since the feature of driverless vehicle repositioning imposes counteracting welfare effects as a response to a tax change. Policymakers and researches are encouraged to take on the challenge of increasing complexity to tackle transport-related inefficiencies in the era of self-driving cars. Full article

In this study, we aimed to evaluate the performance of various machine learning (ML) classifiers to predict mode choice of movement-challenged persons (MCPs) based on data collected through a questionnaire survey of 384 respondents in Dhaka, Bangladesh. The mode choice set consisted of CNG-driven auto-rickshaw, bus, walking, motorized rickshaw, and non-motorized rickshaw, which was found as the most prominent mode used by MCPs. Age, sex, income, travel time, and supporting instrument (as an indicator of the level of disability) utilized by MCPs were explored as predictive variables. Results from the different split ratios with 10-fold cross-validation were compared to evaluate model outcomes. A split ratio of 60% demonstrates the optimum accuracy. It was found that Multi-nominal Logistic Regression (MNL), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) show higher accuracy for the split ratio of 60%. Overfitting of bus and walking as a travel mode was found as a source of classification error. Travel time was identified as the most important factor influencing the selection of walking, CNG, and rickshaw for MNL, KNN, and LDA. LDA and KNN depict the supporting instrument as a more important factor in mode choice than MNL. The selection of rickshaw as a mode follows a relatively normal probability distribution, while probability distribution is negatively skewed for the other three modes. Full article

The increasing need for dynamic in-flight adjustments of a trajectory allows the airport, air traffic control and the airline a high degree of flexibility in terms of in-flight execution. This concept enables numerous optimisation options to jointly meet the requirements of sustainable air transport to increase economic and ecological efficiency, as well as safety. One promising measure is to control the aircraft arrival rate to prevent over-demand in the approach sector around the airport. In so-called Long-Range Air Traffic Management, the arrival time of long-haul flights, in particular, is already controlled many hours before arrival. However, the control options and their effects on arrival time and fuel burn are heavily dependent on flight performance and the (hardly predictable) influence of the weather. In this study, we optimize the arrival time of 26 long-haul flights in the Asia-Pacific region with arrival at Changi Airport within a peak hour considering the arrival rate of medium-haul and short-haul flights. This control is done by speed adjustments and by choosing alternative routes. For the first time, we model each long-haul flight and its control options individually in real weather conditions. We found that speed adjustments should start three to four hours before arriving at the approach sector to maximize the fuel-saving potential of small deviations from the optimal cruising speed, considering the predictability of the arrival time under real weather conditions. Allowing the aircraft to additionally choose an alternative lateral route, different from the filed flight plan, both maximizes the potential for harmonization of the number of aircraft in the approach sector and minimizes the total fuel burn. Unlike speed adjustments, alternative routes changes are effective even during the last hour of the cruise phase. Full article