Future Transp., Volume 4, Issue 4 (December 2024) – 24 articles

The integration of Connected Vehicles into conventional traffic systems presents significant challenges due to the diverse behaviors and objectives of different drivers. Conventional vehicle drivers typically follow User Equilibrium principles, aiming to minimize their individual travel times without considering the overall network impact. In contrast, Connected Vehicle drivers, guided by real-time information from central authorities or private service providers, can adopt System Optimum strategies or Cournot-Nash oligopoly behaviors, respectively. The coexistence of these distinct player classes in mixed-traffic environments complicates the task of achieving optimal traffic flow and network performance. This paper presents a comprehensive framework for optimizing mixed-traffic road networks through a multiclass traffic assignment model. The framework integrates three distinct types of players: conventional vehicle drivers adhering to User Equilibrium principles, Connected Vehicle drivers following System Optimum principles under a central governing authority, and Connected Vehicle drivers operating under Cournot-Nash oligopoly conditions with access to services from private companies. The methodology includes defining a model to achieve optimal mixed equilibria, designing an algorithm for multiclass traffic assignment, formulating strategic games to analyze player interactions, and establishing key performance indicators to evaluate network efficiency and effectiveness. The framework is applied to a real-world road network, validating its practicality and effectiveness through computational results. The extraction and analysis of computational results are used to propose optimal traffic management policies for mixed-traffic environments. The findings provide significant insights into the dynamics of mixed traffic networks and offer practical recommendations for improving traffic management in increasingly complex urban transportation systems. Full article

The emergence of micro-mobility transportation in urban areas has led to a transformative shift in mobility options, yet it has also brought about heightened traffic conflicts and crashes. This research addresses these challenges by pioneering the integration of image-processing techniques with machine learning methodologies to analyze crash diagrams. The study aims to extract latent features from crash data, specifically focusing on understanding the factors influencing injury severity among vehicle and micro-mobility crashes in Michigan’s urban areas. Micro-mobility devices analyzed in this study are bicycles, e-wheelchairs, skateboards, and e-scooters. The AlexNet Convolutional Neural Network (CNN) was utilized to identify various attributes from crash diagrams, enabling the recognition and classification of micro-mobility device collision locations into three categories: roadside, shoulder, and bicycle lane. This study utilized the 2023 Michigan UD-10 crash reports comprising 1174 diverse micro-mobility crash diagrams. Subsequently, the Random Forest classification algorithm was utilized to pinpoint the primary factors and their interactions that affect the severity of micro-mobility injuries. The results suggest that roads with speed limits exceeding 40 mph are the most significant factor in determining the severity of micro-mobility injuries. In addition, micro-mobility rider violations and motorists left-turning maneuvers are associated with more severe crash outcomes. In addition, the findings emphasize the overall effect of many different variables, such as improper lane use, violations, and hazardous actions by micro-mobility users. These factors demonstrate elevated rates of prevalence among younger micro-mobility users and are found to be associated with distracted motorists, elderly motorists, or those who ride during nighttime. Full article

Intersection control systems have been actively studied in recent years as they could potentially replace traffic signals via the utilization of the communication and automatic driving capabilities of connected and autonomous vehicles (CAVs). In these studies, conflicting travel trajectories at intersections that could cause accidents and delays were safely and efficiently avoided by controlling the vehicle’s speed. However, routing approaches for avoiding conflicts at intersections have only been discussed in a few studies. To investigate the feasibility of avoiding intersection conflicts through network-level route allocation, we propose a cooperative route allocation model using reinforcement learning than can model the relationship between the complex traffic environment and optimal route solutions. Models aimed at decreasing the total travel time and those with high delay importance owing to conflicts in travel times were trained and verified under multiple traffic conditions. The results indicate that our model effectively allocates vehicles to their optimal routes, reducing the number of intersection conflicts and decreasing the average travel time by up to approximately 40 s compared to random allocation, demonstrating the potential of reinforcement learning for cooperative route allocation in the management of multiple vehicles. Full article

In the context of increasing research on algorithms for different modules of the autonomous driving stack, the development and evaluation of these algorithms for deployment onboard vehicles is the next critical step. In the development and verification phases, simulations play a pivotal role in achieving this aim. The uncertainty quantification of Autonomous Vehicle (AV) systems could be used to enhance safety assurance and define the error-handling capabilities of autonomous driving systems (ADSs). In this paper, a virtual validation methodology for the control module of an autonomous driving stack is proposed. The methodology is applied to a rule-defined Model Predictive Controller (MPC)-based motion planner, where uncertainty quantification (UQ) is performed across various scenarios, based on the intended functionality within the algorithm’s operational design domain (ODD). The framework is designed to assess the performance of the algorithm under localization uncertainties, while performing obstacle vehicle-overtaking, vehicle-following, and safe-stopping maneuvers. Full article

Florida’s population is projected to grow by 8.8 million residents over the next 25 years. This increase places demands on the transportation system, particularly for mobility-vulnerable populations, potentially impacting equitable transportation options and access for all users. Developing transit solutions for mobility-vulnerable populations may enhance economic activity, health, and well-being. Inquiry is needed to explore transportation stakeholders’ strategies and perspectives on the challenges and opportunities of the existing transportation system. Therefore, this study examined stakeholders’ views on strategies to address user needs, related challenges, and opportunities while identifying gaps, strengths, weaknesses, and threats relevant to addressing transportation among mobility-vulnerable populations. The team conducted 13 semi-structured interviews with city planners, transportation employees, industry stakeholders, and state representatives. Findings indicate gaps in the transportation system, particularly available funding and limited common goals among stakeholders. Participants emphasized the need for enhanced educational resources and collaboration with community members. They identified strengths like a willingness to pilot innovative transit technologies, weaknesses such as unreliable options, opportunities for innovation, and threats, including COVID-19′s impact on transportation use. Understanding transportation stakeholders’ shared challenges and opportunities is crucial for identifying transit needs and developing strategies to reduce disparities for mobility-vulnerable populations. Full article

The development and land use surrounding airports are a concern and interest for airport operators, public communities, business communities, and local authorities. Airport development and operations are governed by both national and international regulations that often extend beyond airport property boundaries. Typical international airports’ regulations, recommendations, and guidance documents (e.g., Noise Exposure and Obstacle Limitation Surfaces) and their national counterparts focus on airport land-use planning. Individual third-party risk assessment of airport operations serves as a complementary tool to these regulations, providing means to assess and manage land-use compatibility and control activities near airport perimeters. Developing robust risk assessment models is essential for defining and validating public safety areas and Runway Protection Zones to ensure land-use compatibility and public safety. Although several quantitative risk assessment models exist, significant differences remain in their methodologies and applications. Over the past 20 to 35 years, most models have evolved based on historical data from aircraft accidents. This article provides a comprehensive review of risk analysis methods for areas surrounding airports and presents a quantitative comparison of two specific approaches, the ENAC/Sapienza and ACRP methods, along with their associated calculation software. Full article

Flexible public transport is defined as a future mobility solution that adapts to user needs and the fluctuating demand patterns that mainly appear in rural areas. However, the temporal variations in traveler preferences for flexible bus services remain largely unexplored in existing research. This constrains the realization of adaptive and customized solutions. Therefore, this study attempts to develop a distinct method for strategic planning of a flexible bus service. To this end, a combinatorial method is undertaken: quantitative social research (questionnaires) and spatial analysis. This combinatorial approach is applied at Korinth and Loutraki in Greece, two significant rural areas neighboring the Athens Metropolitan Area. The results signify that cost and time are the most crucial factors affecting the use of a flexible service. Furthermore, respondents preferred a door-to-door service in the morning and a stop-based service in the afternoon/evening. Concerning route planning, eight routes with different purposes are suggested (e.g., train feeder, touristic, etc.) covering adequately both urban and rural parts of the study area. Notably, the applied methodological approach can be a guideline for planners and policymakers, assisting them in finding effective strategies for introducing flexible public transport in rural areas, especially in contexts where collective transport culture is limited. Full article

The car rental sector is a dynamic and rapidly growing business sector, which is important for both the development of the automotive industry and consumer mobility needs. In the rental car market, risk management becomes an essential factor determining the success and long-term growth of business operations. Risk factors inherent in this sector, such as the technical condition of cars, customer behavior, economic conditions and the legal environment, require a structured and efficient management that would allow companies to make the most of the available opportunities and to minimize threats. The purpose of this article is to examine the risk management of car rental and evaluate the impact of a 5–10-year-old car rental model with reverse logistics on risk reduction and business efficiency. The article examines the methods of the risk management process, presenting the methods of risk identification, assessment and reduction, as well as the possibilities of applying reverse logistics in the car rental sector. The results of the applied expert method of quantitative research and the conducted questionnaire survey revealed the factors that are most important for a company that renews its fleet of vehicles. These are the price, operating costs, sustainability, and environmental friendliness, with sustainability being the top priority. Companies can benefit from the research results when making decisions about the renewal and optimization of their vehicle fleet. It was concluded that implementing reverse logistics in the car rental sector will increase company profits and reduce pollution. Full article

Transport Demand Management (TDM) is crucial in shaping travel behavior and enhancing urban mobility by promoting sustainable transport options. This study represents a comprehensive analysis of employee commuting behavior across seventy-seven cities in Italy, with a focus on Rome as a case study. It investigates some requirements of the workplace travel plan as a TDM strategy for promoting sustainable commuting. An online survey conducted in June 2022 yielded 2314 valid responses, including 1320 from private car drivers. K-means clustering was used to identify distinct behavioral patterns among commuters, revealing four clusters based on demographic factors and transport preferences, such as age, gender, family circumstances, vehicle ownership, willingness to walk, ride bicycles, or e-scooters, and reasons for mode choice. This study analyzed Rome’s public transport network, land use, and private car use. Results underscore the need for tailored transport policies that enhance inclusivity and accessibility, especially for employees with family members who cannot commute independently. A spatial analysis of Rome reveals significant infrastructure deficiencies, such as complicated transfers and inaccessible stations, which discourage PT use. Future research should explore the impact of remote work and psychological factors and conduct in-depth subgroup analyses to inform inclusive transport policy development. Full article

Yaoundé, the capital of Cameroon, is one of the cities in the country most affected by road traffic crashes. Despite the measures taken by authorities, the human factor remains a major cause of these crashes. This study aimed to evaluate the measures taken to reduce the risk-taking behaviors of collective taxi drivers in Yaoundé. A survey of 144 collective taxi drivers was conducted to gather information on their driving habits, adherence to, and perceived effects of safety regulations. The study revealed the following prevalence of risky driving behaviors among collective taxi drivers: 41.33% for impaired driving; 67% for speeding, 62% for disobeying traffic lights, 68.86% for distraction; and 67% for risky maneuvering on the road. Significant associations were found between risk perceptions and involvement in risky driving behaviors. Associations were also established between the frequency of police inspections and involvement in risky behaviors, between the participation in training programs on safety issues and using poorly maintained vehicles, and between the frequency of awareness campaigns and poor maneuvering on the road. To address these issues, it is essential to strengthen preventive measures on risk factors, raise awareness on a large scale and on a regular basis, and strictly enforce the existing regulations. Full article

This paper proposes the development of a smart traffic light prototype based on vehicular traffic flow measurement in the stretch between two avenues in the city of Limeira, SP, Brazil, focusing on the stretch towards UNICAMP’s School of Technology. To this end, we initially developed a Python code using the OpenCV library in order to detect and count vehicles. With the counting in operation, programming logic was inserted, aiming at preparing traffic light timers based on vehicular traffic. Finally, the traffic lights were added to display video via a code change to show the ongoing color changes, also obtaining a code for identifying vehicles and flow, in addition to the virtual traffic light system itself in the system. Vehicle counting accuracy was 75% for large vehicles, 90% for passenger cars, and 100% for motorcycles. The simulation of a smart traffic light implementation worked satisfactorily according to the post-processing of the video recorded for validation. Full article

This article investigates viable solutions to implement an Urban Air Mobility network in Milan, Italy, and analyzes its influence on the airspace capacity. The network comprises eight vertiports for passenger transport among two main airports in the area and the city using electric vertical take-off and landing aircraft (eVTOLs). A Fast-Time Simulation (FTS) model with the software AirTOp (Air Traffic Optimization) allowed the evaluation of the ideal capacity of the network by varying two configurations, which differ from each other in terms of the number of Final Approach and Takeoff areas (FATOs). The results show how it is possible to reach high hourly capacities (in the order of one hundred), thus allowing the use of the service for about 4% of the total passengers passing through the two airports during the reference day chosen for this study. However, the results are ideal due to the strong idealism of the system, which overlooks several factors, and they should be considered as the maximum limit that can be obtained. Despite this, the method presented in this article can also be adapted for other urban areas with high population densities. In addition, the use of a simulation tool of this type allows, in addition to a numerical analysis, a qualitative analysis of the network behavior in terms of traffic, thus highlighting the criticalities of the proposed systems. Full article

This paper explores the management of digitalization projects within the railway industry. It aims to increase and understand the opportunities presented by digitalization and automation in rail operations. Employing a scoping review methodology, this research investigates the execution of European Rail Traffic Management System (ERTMS), remote train control (RTC), and automatic train operation (ATO) projects spanning from 2005 to 2023, with a particular emphasis on metro automation, the remote control of freight and passenger trains, fully automated trains, and highly assisted driving. The refined selection process yielded 30 papers. The analysis of the retrieved papers identified managerial issues, with stakeholder management, change management, and organizational management emerging as recurring themes. Despite the increasing trend in publications, the limited representation managerial issues in ERTMS, RTC, and ATO projects in scientific research persists, with implications for the industry’s advancement. This research sheds light on the critical intersection of change management and digitalization within the railway industry by showing the impact of ERTMS, RTC, and ATO on organizational and scope dynamics. The need for human-centered systems is highlighted, showing the necessity of involving every echelon of the organization in the change management process. These findings provide insights for practitioners, researchers, and policymakers, emphasizing the need for understanding and addressing managerial aspects for successful and sustainable digitalization implementations. Full article

Capturing the value infrastructure investments add to the residential market is a longstanding policy to defray their expense. Unfortunately, estimates of the added value of infrastructure, generally, and estimates of the added value of walkability, specifically, are scarce. Novel, multiscale models free independent variables to manifest simultaneously at different scales of analysis to greatly improve specifications to precisely estimate walkability valuation. Results from analysis of years of transactions within walking distance of heavy rail stations suggest that walkability adds value available for capture locally, not systemically. Stakeholders confront myriad problems to replicate the accessibility characteristics shown to add value given the distinct cluster where walkability adds value available for capture in a heavy rail system. Full article

The study on individual mobility patterns supports our better understanding of spatiotemporal characteristics of people’s travel behavior and social activities. The mobile phone GPS data are advantageous due to the large size of their data coverage. This paper aims to identify individual activity anchor places and to analyze related patterns based on the GPS data collected from thousands of mobile phone users over four months in Greater Paris, France. We propose a method to refine the identification of home and secondary activities. Based on this, the mobility spatial characteristics are aggregated by applying a three-stage clustering method. As a consequence, the clusters of activity types, the daily mobility patterns (day types), and the user groups with similar daily mobility patterns are obtained stage by stage. This allows us to analyze the obtained clusters in a cascading maneuver by three different levels: activity level, day level, and individual level. Inversely, the mobility characteristics per user group are interpreted with respect to the interpretation of day types and then activity types. From the interpretable clusters, it is facilitated for us to find the daily mobility differences by user groups across weekdays and weekends, transport modes, as well as the mobility variability over the study period. Full article

Many transportation agencies have deployed pan–tilt–zoom (PTZ) closed-circuit television (CCTV) cameras to monitor roadway conditions and coordinate traffic incident management (TIM), particularly in urbanized areas. Pre-programmed “presets” provide the ability to rapidly position a camera on regions of highways. However, camera views occasionally develop systematic deviations from their original presets due to a variety of factors, such as camera change-outs, routine maintenance, drive belt slippage, bracket movements, and even minor vehicle crashes into the camera support structures. Scheduled manual calibration is one way to systematically eliminate these positioning problems, but it is more desirable to develop automated techniques to detect and alert agencies of potential drift. This is particularly useful for agencies with large camera networks, often numbering in the 1000’s. This paper proposes a methodology using the mean Structured Similarity Index Measure (SSIM) to compare images for a current observation to a stored original image with identical PTZ coordinates. Analyzing images using the mean SSIM generates a single value, which is then aggregated every week to generate potential drift alerts. This methodology was applied to 2200 images from 49 cameras over a 12-month period, which generated less than 30 alerts that required manual validation to determine the confirmed drift detection rate. Approximately 57% of those alerts were confirmed to be camera drift. This paper concludes with the limitations of the methodology and future research opportunities to possibly increase alert accuracy in an active deployment. Full article

This paper investigates a practical last-mile delivery scenario where a fleet of trucks replenishes autonomous mobile parcel lockers (AMPLs) in an urban setting. The lockers move along specified paths within restricted zones to reach customers’ locations. Ensuring seamless coordination between trucks and AMPLs requires the identification of suitable locations to exchange empty or loaded modular lockers. We first introduce a mixed-integer linear programming (MILP) formulation for the investigated problem. The proposed formulation establishes the basis for optimizing meeting point selection and routing decisions. Additionally, the study introduces a cluster-based simulated annealing (CSA) algorithm tailored for addressing larger-scale instances of the studied problem. The CSA algorithm incorporates the K-means clustering method with specialized operators rooted in an extensive neighborhood search, aiming to improve the effectiveness of solution discovery. We generated a new set of benchmark instances to assess the MILP formulation’s efficiency and the proposed metaheuristic algorithm and conducted comprehensive numerical experiments. Full article

Despite the growing adoption of Mobility as a Service (MaaS) in urban transportation systems, standard monitoring methods for evaluating its impact and effectiveness still need to be developed. This study proposes a quantitative state of MaaS analysis based on mobility market indicators and macroeconomic metrics to generate a MaaS Status Index (MSI). The intention is to introduce a standardised quantitative methodology for systematically assessing and comparing the state of MaaS in urban mobility systems. The MSI aims to quantitatively capture the economic, social, technological, and infrastructural conditions relevant to MaaS implementation. The methodology includes four steps: identifying relevant mobility markets, defining mobility market metrics, integrating macroeconomic metrics, and deriving the MSI formula. We apply the MSI methodology to the Austrian mobility market as a case study, demonstrating its practicality in assessing MaaS readiness and highlighting specific challenges and opportunities within the Austrian mobility system. The analysis covers the present (2017–2022) and the projected future (2023–2028). The findings indicate that the proposed MSI is an effective tool for evaluating the readiness of MaaS implementation. Full article

Urban pollution awareness is a subject of widespread debate, particularly regarding the role of future urban transportation. In recent years, local policymakers and private operators have implemented various measures to address the negative impacts of transportation, including promoting micro-shared mobility services. Our research investigates the factors influencing citizens’ decisions to use these services, focusing on e-bike sharing and e-scooter sharing. We collected data on individual mobility patterns in Italian cities and administered hypothetical choice tasks to examine revealed and stated preferences. Our findings highlight the most influential factors guiding users’ decisions and identify the preferred sharing option between e-bikes and e-scooters. The implications of our results could provide valuable insights for local regulators and shared mobility operators in designing effective and sustainable future transportation policies. Full article

The innovative trend of “as a service” due to digital development and the rise of issues such as air pollution and traffic congestion led to the emergence of Mobility as a Service (MaaS) in the transportation sector. Companies and governments are experimenting to create a sustainable and efficient transportation future with MaaS. However, MaaS realization and business success from MaaS are still in their growing phase, making this study particularly relevant and timely. This study aims to identify the attributes of users’ selection of integrated mobility app services and the MaaS attributes that affect the behavioral intention to use through the mediation of perceived usefulness and perceived ease of use. This study marked four selection attributes—habit-congruence, information accuracy, relative advantage on efficiency, and IT system quality—for the integrated mobility app service, and 315 actual users of integrated mobility apps in Republic of Korea were sampled and analyzed. In terms of influence, information accuracy, relative advantage on efficiency, and habit-congruence significantly impacted perceived usefulness, in which habit-congruence had the most significant impact on perceived ease of use. In addition, habit-congruence and information accuracy were found to positively affect the behavioral intention to use, mediated by perceived usefulness and perceived ease. We also found that IT system quality was not a user selection attribute where this study was conducted. By providing empirical findings, this study can give management guidelines to companies and researchers in developing integrated mobility app service strategies to increase the number of users and maintain long-term customer relationships. Full article

This work investigates the risk to Vulnerable Road Users (VRUs) from a novel light rail vehicle using the pedestrian impact scenario outlined in CEN/TR 17420. At a 20 km/h impact speed, a maximum head impact criterion (HIC₁₅) value of 15.9 was obtained for a 50th-percentile anthropometric test device (ATD), with this value increasing to 120.2 at 30 km/h impact speed. Both results are within the CEN/TR 17420 prescribed limit of 1000. In both cases, the vehicle does not fully comply with CEN/TR 17420 recommendations due to insufficient lateral displacement of the ATD post-impact. A vehicle front-end design—which would be exempt from the CEN/TR 17420 impact testing—was designed and tested to the same framework. Despite being formally exempt from testing, the design also did not fully comply with CEN/TR 17420 lateral displacement requirements. Critical evaluation of the CEN/TR 17420 framework is presented, leading to recommendations about how updated frameworks should take a pragmatic approach in how they define VRUs, and the measurement criteria used for assessing VRU risk in collisions. Discussions are presented considering whether alternative frameworks, such as the Bus Safety Standard, should be applicable to assess the safety of the novel light rail vehicle. Full article

This paper reports on a study investigating transportation professionals’ perceptions of AI’s equity impacts in the transportation sector, focusing on demographic variations in views. A survey conducted among U.S. transportation professionals examined their attitudes toward AI’s potential to influence transportation equity and ethics. The findings reveal insights based on gender, employment sector, educational background, and AI knowledge level, with notable differences in confidence towards AI’s ability to reduce bias and engage communities. This research highlights a commonly held opinion that there is a limited understanding of AI ethics within the transportation community, emphasizing the need for ongoing education and adaptation to AI technologies. This study contributes valuable perspectives to the discourse on AI, equity, and ethics in transportation, offering a foundation for future policy and strategy development. Full article

Increasing environmental and economic pressures have led to numerous innovations in the logistics sector, including integrated people and freight transport (IPFT). Despite growing attention from practitioners and researchers, IPFT lacks extensive research coverage. This study aims to bridge this gap by presenting a general framework and making several key contributions. It identifies, researches, and explains relevant terminologies, such as cargo hitching, freight on transit (FoT), urban co-modality, crowd-shipping (CS), occasional drivers (OD), crowdsourced delivery among friends, and share-a-ride, illustrating the interaction of IPFT with different systems like the sharing economy and co-modality. Furthermore, it classifies IPFT-related studies at strategic, tactical, and operational decision levels, detailing those that address uncertainty. The study also analyzes the opportunities and challenges associated with IPFT, highlighting social, economic, and environmental benefits and examining challenges from a PESTEL (political, economic, social, technological, environmental, and legal) perspective. Additionally, it discusses practical applications of IPFT and offers recommendations for future research and development, aiming to guide practitioners and researchers in addressing existing challenges and leveraging opportunities. This comprehensive framework aims to significantly advance the understanding and implementation of IPFT in the logistics sector. Full article

In the realm of traffic signal operations, the Signal Timing Manual second edition (STM2) serves as a foundational guide for professionals engaged in multimodal signal retiming projects. However, it is acknowledged that the STM2 has its limitations, and real-world conditions often necessitate adaptations in the established procedures. Considering this context, this research endeavors to bridge this gap by conducting a comprehensive survey aimed at traffic signal professionals. This study presents the findings of a comprehensive survey conducted among traffic signal professionals to explore the methodologies, challenges, and practices involved in multimodal signal retiming projects. The survey aimed to obtain detailed insights into the current state of signal retiming, the types of data and tools utilized, and the adaptations necessary to address the complexities of multimodal urban transportation networks. The survey highlights and summarizes responses from 36 professionals across North America, providing insight into both the common strategies and unique challenges faced by those responsible for optimizing signal timings in diverse and dynamic urban environments. The survey results reveal a reliance on diverse tools and data types for signal optimization, highlighting the complexities of accommodating different transportation needs. The findings underscore the importance of tailored approaches and advanced technologies in enhancing signal retiming processes. The insights gained from this study will inform future strategies and enhance the effectiveness of signal retiming procedures in urban areas, thereby contributing to improved traffic management and multimodal transportation efficiency. Full article