Future Transp., Volume 1, Issue 3 (December 2021) – 22 articles

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

This paper investigates the opportunities offered by floating car data (FCD) to infer delivering activities. A discrete trip-chain order model (within the random utility theory) for light goods vehicles (laden weight less than 3.5 tons) is hence proposed, which characterizes delivery tours in terms of the number of stops/deliveries performed. Thus, the main goal of the study is to calibrate a discrete choice model to estimate the number of stops/deliveries per tour by using FCD, which can be incorporated in a planning procedure for obtaining a preliminary assessment of parking demand. The data used refer to light goods vehicles operating in the Veneto region. The database contains more than 8000 tours undertaken in 60 working days. Satisfactory results have been obtained in terms of tour estimation and model transferability. Full article

Urban bicycling has been largely marginalized for decades in the global north and south. Despite a renaissance over the last two decades in academic research, political discourse, sustainability activism, and planning, cities often struggle with data quality and quantity. Digitalization has led to more and better data sources, but they still must be validated and compared with findings from conventional travel surveys. With the COVID-19 pandemic, bicycling and associated road facilities expanded, as did road crashes involving bicycles. This study utilized tens of thousands of datapoints sourced by public institutions and digital devices belonging to private companies that have spread across Berlin over the last ten years and are currently ubiquitous. What does an integrated analysis of data from these novel sources reveal for urban bicycling research, planning, and network design? We explored and visualized the relationships and spatiotemporal variations in (i) bicycling volumes and (ii) crashes, unveiling the (iii) distribution of and correlation between datasets and the city’s bikeway network at an unprecedented threshold. The findings can be useful for special interest groups and to guide future urban bicycling research, planning, and network design. Full article

This study reviews obstacle detection technologies in vegetation for autonomous vehicles or robots. Autonomous vehicles used in agriculture and as lawn mowers face many environmental obstacles that are difficult to recognize for the vehicle sensor. This review provides information on choosing appropriate sensors to detect obstacles through vegetation, based on experiments carried out in different agricultural fields. The experimental setup from the literature consists of sensors placed in front of obstacles, including a thermal camera; red, green, blue (RGB) camera; 360° camera; light detection and ranging (LiDAR); and radar. These sensors were used either in combination or single-handedly on agricultural vehicles to detect objects hidden inside the agricultural field. The thermal camera successfully detected hidden objects, such as barrels, human mannequins, and humans, as did LiDAR in one experiment. The RGB camera and stereo camera were less efficient at detecting hidden objects compared with protruding objects. Radar detects hidden objects easily but lacks resolution. Hyperspectral sensing systems can identify and classify objects, but they consume a lot of storage. To obtain clearer and more robust data of hidden objects in vegetation and extreme weather conditions, further experiments should be performed for various climatic conditions combining active and passive sensors. Full article

This study investigates the perceived safety of passengers while being on board of a driverless shuttle without a steward present. The aim of the study is to draw conclusions on factors that influence and contribute to perceived safety of passengers in driverless shuttles. For this, four different test rides were conducted, representing aspects that might challenge passengers’ perceived safety once driverless shuttles become part of public transport: passengers had to ride the shuttle on their own (without a steward present), had to interact with another passenger, and had to react to two different unexpected technical difficulties. Passengers were then asked what had influenced their perceived safety and what would contribute to it. Results show that perceived safety of passengers was high across all different test rides. The most important factors influencing the perceived safety of passengers were the shuttle’s driving style and passengers’ trust in the technology. The driving style was increasingly less important as the passengers gained experience with the driverless shuttle. Readily available contact with someone in a control room would significantly contribute to an increase in perceived safety while riding a driverless shuttle. For researchers, as well as technicians in the field of autonomous driving, our findings could inform the design and set-up of driverless shuttles in order to increase perceived safety; for example, how to signal passengers that there is always the possibility of contact to someone in a control room. Reacting to these concerns and challenges will further help to foster acceptance of AVs in society. Future research should explore our findings in an even more natural setting, e.g., a controlled mixed traffic environment. Full article

Time-sensitive parcel deliveries—shipments requested for delivery in a day or less—are an increasingly important aspect of urban logistics. It is challenging to deal with these deliveries from a carrier perspective. These require additional planning constraints, preventing the efficient consolidation of deliveries that is possible when demand is well known in advance. Furthermore, such time-sensitive deliveries are requested to a wider spatial scope than retail centers, including homes and offices. Therefore, an increase in such deliveries is considered to exacerbate negative externalities, such as congestion and emissions. One of the solutions is to leverage spare capacity in passenger transport modes. This concept is often denominated as cargo hitching. While there are various system designs, it is crucial that such a solution does not deteriorate the quality of service of passenger trips. This research aims to evaluate the use of mobility-on-demand (MOD) services that perform same-day parcel deliveries. To test the MOD-based solutions, we utilize a high-resolution agent- and activity-based simulation platform of passenger and freight flows. E-commerce demand carrier data collected in Singapore are used to characterize simulated parcel delivery demand. We explore operational scenarios that aim to minimize the adverse effects of fulfilling deliveries with MOD service vehicles on passenger flows. Adverse effects are measured in fulfillment, wait, and travel times. A case study on Singapore indicates that the MOD services have potential to fulfill a considerable amount of parcel deliveries and decrease freight vehicle traffic and total vehicle kilometers travelled without compromising the quality of MOD for passenger travel. Insights into the operational performance of the cargo-hitching service are also provided. Full article

Standard driving cycles (DCs) and real driving emissions (RDE) legislation developed by the European Commission contains significant gaps with regard to quantifying local area vehicle emission levels and fuel consumption (FC). The aim of this paper was to review local DCs for estimating emission levels and FC under laboratory and real-world conditions. This review article has three sections. First, the detailed steps and methodologies applied during the development of these DCs are examined to highlight weaknesses. Next, a comparison is presented of various recent local DCs using the Worldwide Harmonized Light-Duty Test Cycle (WLTC) and FTP75 (Federal Test Procedure) in terms of the main characteristic parameters. Finally, the gap between RDE with laboratory-based and real-world emissions is discussed. The use of a large sample of real data to develop a typical DC for the local area could better reflect vehicle driving patterns on actual roads and offer a better estimation of emissions and consumed energy. The main issue found with most of the local DCs reviewed was a small data sample collected from a small number of vehicles during a short period of time, the lack of separate phases for driving conditions, and the shifting strategy adopted with the chassis dynamometer. On-road emissions measured by the portable emissions measurement system (PEMS) were higher than the laboratory-based measurements. Driving situation outside the boundary conditions of RDE shows higher emissions due to cold temperatures, road grade, similar shares of route, drivers’ dynamic driving conditions, and uncertainty within the PEMS and RDE analysis tools. Full article

Studies have been implemented in the literature to enhance the safety of traffic barriers by identification of the contributory factors to those crashes. However, almost all those past studies are subject to potential pitfalls of just answering the cause-and-effect question by traditional statistical methods, which fail to account for possible endogeneity. Modeling traffic barrier crash severity with traditional statistical methods might be biased as many parameters such as barrier’s types is endogenous to unseen factors including policy makers’ decisions in allocating those barriers. Not accounting for the possible endogeneity in the dataset and ignoring correlation between regressors’ error terms might result in biased or erroneous coefficients’ estimates. That is especially true in the presence of strong correlations across models’ error terms. Thus, this study was conducted to model barriers’ crash severity by taking into consideration the endogeneity and correlations across the models’ error terms. Here, the trivariate copula-based method was implemented to simultaneously model traffic barrier crash severity, shoulder width and barrier’s types, while accounting for interrelationships across the models’ error terms. The results provide strong evidence of correlations between the unseen factors to the selections of barrier’s types, shoulder width installation, and crash injury levels. For instance, we found in the presence of accounting for endogeneity and correlation between unseen factors, concrete traffic barrier type and higher shoulder width installation are negatively correlated with unseen factors contributing to severe barriers’ crashes That is despite the fact that the observed factors of those predictors were found to have a reverse impact on the severity of barriers’ crashes. Full article

This paper presents the framework for a dynamic Advanced Traveler Information System (ATIS). The ATIS currently in use provides users with stereotyped travel options, but the set of available modes in a given place and time is not the same for each traveler, and such a personal choice set varies within the context of daily trip chains. The research presented in this paper addressed these limitations by including dynamic features in the proposed system. The activity chain that the user performs as well as the personal mode availabilities are modelled simultaneously to define the logical architecture of an innovative information system. Such a technology was intended to assist travelers in performing their daily trip chaining. In order to provide some insight regarding the efficacy of the proposed procedure, a pilot test was performed using real travel time information. Results have shown that the ATIS proposed in this study might generate a significant reduction in travel times. Full article

Winter conditions create hazardous roads that municipalities work hard to maintain to ensure the safety of the travelling public. Targeting their efforts with effective network screening will help transportation managers address these problems. In our recent efforts, regression kriging was found to be a viable and effective network screening methodology. However, the study was constrained by its limited spatial extent making the reported results less conclusive and transferrable. In addition, our previous work implemented what has long been adopted in most of conventional studies—the Euclidean distance; however, use of the road network distance would, intuitively, result in further improving kriging estimates, especially when dealing with transportation problems. Therefore, this study improves upon our previous efforts by developing a more advanced kriging model; namely, network regression kriging using the entire state of Iowa with the significantly expanded road network. The transferability of the developed models is also explored to investigate its generalization potential. The findings based on various statistical measures suggest that the enhanced kriging model vastly improved the estimation performance at the cost of greater computational complexity and run times. The study also suggests that regional semivariograms better represent the true nature of the local variances, though an overall model may still function adequately if higher fidelity is not required. Full article

The literature review highlighted the impacts of drivers’ behavior on passengers’ attitudes in the choice of seatbelt usage. However, limited studies have been done to determine those impacts. Studying the passengers’ seatbelt use is especially needed to find out why passengers choose not to buckle up, and consequently it helps decision makers to target appropriate groups. So, this study was conducted to find drivers’ characteristics that might impact the passenger’s seatbelt use, in addition to other passengers’ characteristics themselves. While performing any analysis, it is important to use a right statistical model to achieve a less biased point estimate of the model parameters. The latent class multinomial logit model (LC-MNL) can be seen as an alternative to the mixed logit model, replacing the continuous with a discrete distribution, by capturing possible heterogeneity through membership in various clusters. In this study, instead of a response to the survey or crash observations, we employed a real-life observational data for the analysis. Results derived from the analysis reveal a clear indication of heterogeneity across individuals for almost all parameters. Various socio-demographic variables for class allocation and models with different latent numbers were considered and checked in terms of goodness of fit. The results indicated that a class membership with three factors based on vehicle type would result in a best fit. The results also highlighted the significant impacts of driver seatbelt status, time of a day, distance of traveling, vehicle type, and driver gender, instead of passenger gender, as some of the factors impacting the passengers’ choice of seatbelt usage. In addition, it was found that the belting status of passengers is positively associated with the belting condition of drivers, highlighting the psychological behavioral impact of drivers on passengers. Extensive discussion has been made regarding the implications of the findings. Full article

Terminals are key components of intermodal transport networks, as they are the facilities where freight is transferred between transport modes. The efficiency of such facilities crucially depends on their locations (and sizes), which are typically chosen considering two levels of analysis: local/urban and regional/country. Our focus in this presentation is the regional/country level. At this level, the problems involved in the locational (and sizing) decisions at stake are a particular variety of hub locations problems—a class of problems that has been widely studied through optimization approaches. However, they typically assume that decisions are made in a centralized management context: decision-makers not only choose the locations of intermodal terminals (or hubs), but also fully control their utilization (i.e., which terminal each user will patronize). This signifies that such approaches are not applicable when users–potentially, any companies that move freight–behave according to their own individual interests; that is, they are not applicable in a decentralized management context. In this presentation, we describe an ongoing study where (regional) intermodal terminal location problems are dealt with in this type of context considering terminals of different types and respective capacity and operation ranges. In particular, we present the complex optimization model we have developed to handle such problems, and the (sometimes counterintuitive) results it led to when applied to a case study inspired by the Portuguese reality. Full article

Finding a sustainable mobility solution for the future is one of the most competitive challenges in the logistics and mobility sector at present. Policymakers, researchers, and companies are working intensively to provide novel options that are environmentally friendly and sustainable. While autonomous car-sharing services have been introduced as a very promising solution, an innovative alternative is arising: the use of self-driving bikes. Shared autonomous cargo-bike fleets are likely to increase the livability and sustainability of the city, as the use of cargo-bikes in an on-demand mobility service can replace the use of cars for short-distance trips and enhance connectivity to public transportation. However, more research is needed to develop this new concept. In this paper, we investigate different rebalancing strategies for an on-demand, shared-use, self-driving cargo-bikes service (OSABS). We simulate a case study of the system in the inner city of Magdeburg using AnyLogic. The simulation model allows us to evaluate the impact of rebalancing on service level, idle mileage, and energy consumption. We conclude that the best proactive rebalancing strategy for our case study is to relocate bikes only between neighboring regions. We also acknowledge the importance of bike relocation to improve service efficiency and reduce fleet size. Full article

Many small and midsized cities around the world are expected to experience rapid shrinking and aging of their populations in the near future. In Japan, these dramatic demographic shifts have already begun in all but the largest cities, creating an urgent need to redesign public transportation systems to accommodate the transit needs of smaller, older populations. Here we focus on the specific case of Takamatsu, a medium-sized city with a population of 420,000 that is currently redesigning its transit system to better serve an aging city with a declining working-age population. We use the agent-based transportation simulation model MATSim to predict the ramifications of Takamatsu’s transit system reforms on transportation behavior in the year 2050. Our analysis reveals how the effects of Takamatsu’s transit reforms vary geographically and temporally, with societal implications—particularly for the mobility of elderly residents—that we discuss. Full article

Impact analysis of the construction and commissioning of a rail freight infrastructure must contemplate the regional economic system in its entirety, since that impact extends beyond the direct contribution to the increase in production, and the consequent generation of income from capital and remuneration of labor incorporated in such activity. The objective of this research is to know the effects that a rail freight infrastructure produces in a region with a low population, such as Extremadura (Spain). For this purpose, the calculation of the total impact produced by the rail freight corridor on the regional economy of Extremadura has been made, which will correspond to the sum of the direct, indirect and induced impacts. For its determination, a simulation tool has been created from Input-Output tables that allow you to compare different impact levels depending on the intensity of the investments made, or the activity developed following the launch of the new rail corridor. The research concludes with the profitability of the action because for a planned investment of 160 M€ for 3 years in the freight rail corridor from Extremadura, every euro invested returns in more than 2 euros to the Extremadura’s productive system, creating more than 4100 jobs. Full article

Traffic safety studies need more than what the current micro-simulation models can provide, as they presume that all drivers exhibit safe behaviors. Therefore, existing micro-simulation models are inadequate to evaluate the safety impacts of managed motorway systems such as Variable Speed Limits. All microscopic traffic simulation packages include a core car-following model. This paper highlights the limitations of the existing car-following models to emulate driver behaviour for safety study purposes. It also compares the capabilities of the mainstream car-following models, modelling driver behaviour with precise parameters such as headways and time-to-collisions. The comparison evaluates the robustness of each car-following model for safety metric reproductions. A new car-following model, based on the personal space concept and fish school model is proposed to simulate more accurate traffic metrics. This new model is capable of reflecting changes in the headway distribution after imposing the speed limit from variable speed limit (VSL) systems. This model can also emulate different traffic states and can be easily calibrated. These research findings facilitate assessing and predicting intelligent transportation systems effects on motorways, using microscopic simulation. Full article

This study investigates the impacts of the noticeable change in mobility during the COVID-19 pandemic with analyzing its impact on the spatiotemporal patterns of crashes in four demographically different counties in Florida. We employed three methods: (1) a Geographic Information System (GIS)-based method to visualize the spatial differences in crash density patterns, (2) a non-parametric method (Kruskal–Wallis) to examine whether the changes in crash densities are statistically significant, and (3) a negative binomial regression-based approach to identify the significant socio-demographic and transportation-related factors contributing to crash count decrease during COVID-19. Results confirm significant differences in crash densities during the pandemic. This may be due to maintaining social distancing protocols and curfew imposement in all four counties regardless of their sociodemographic dissimilarities. Negative binomial regression results reveal that the presence of youth populations in Leon County are highly correlated with the crash count decrease during COVID-19. Moreover, less crash count decrease in Hillsborough County U.S. Census blocks, mostly populated by the elderly, indicate that this certain age group maintained their mobility patterns, even during the pandemic. Findings have the potential to provide critical insights in dealing with safety concerns of the above-mentioned shifts in mobility patterns for demographically different areas. Full article