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"Transportation services"
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Automatic License Plate Recognition System for Vehicles Using a CNN
Automatic License Plate Recognition (ALPR) systems are important in Intelligent Transportation Services (ITS) as they help ensure effective law enforcement and security. These systems play a significant role in border surveillance, ensuring safeguards, and handling vehicle-related crime. The most effective approach for implementing ALPR systems utilizes deep learning via a convolutional neural network (CNN). A CNN works on an input image by assigning significance to various features of the image and differentiating them from each other. CNNs are popular for license plate character recognition. However, little has been reported on the results of these systems with regard to unusual varieties of license plates or their success at night. We present an efficient ALPR system that uses a CNN for character recognition. A combination of pre-processing and morphological operations was applied to enhance input image quality, which aids system efficiency. The system has various features, such as the ability to recognize multi-line, skewed, and multi-font license plates. It also works efficiently in night mode and can be used for different vehicle types. An overall accuracy of 98.13% was achieved using the proposed CNN technique.
Journal Article
Tools and Methodologies for the Analysis of Home-to-Work Shuttle Service Impacts: The ENEA “Casaccia” Case Study
Mobility management is a regulatory framework designed to streamline systematic mobility and mitigate energy, environmental and economic impacts. In this work, we propose a flexible methodology for evaluating the sustainability of home-to-work travel, providing a comprehensive and detailed ex post cost–benefit assessment. Specifically, we analyzed the effectiveness of the shuttle service operating in the ENEA “Casaccia” Research Centre in pre-pandemic times. Initially, we conducted an online survey to collect data with the aim of characterizing the travel behavior of the staff and reconstructing the multi-modal individual mobility patterns. Over 70% of the recipients, which amounted to about 950 individuals, completed the survey. Subsequently, we studied two alternative scenarios—with and without the shuttle service—comparing their total mileage, energy consumption, and pollutant emissions and performing an economic analysis. Our findings suggest that operating the service has a significant impact on air pollutants and greenhouse gas emissions, with reductions of 97% for volatile organic compounds, 72% for particulate matter, and 60% for carbon dioxide. Moreover, the cost–benefit analysis reveals that both users and the community reaped benefits from the provision of the collective service. These benefits are estimated to be almost EUR 1.35 M per year.
Journal Article
Leader-based diffusion optimization model in transportation service procurement under heterogeneous drivers’ collaboration networks
One of the key issues in transportation systems is allocating shipping orders to the most appropriate drivers in the shortest time and with the maximum profit. Many studies were carried out in the transportation service procurement process for allocating orders, but none of them considered driver-to-driver interactions and applied information diffusion concepts as a framework to maximize the profit, due to the lack of a framework to model the interactions. In this paper, we present a weighted drivers’ collaboration network to form the interactions. To predict the behavior of drivers, a new community detection algorithm is developed to extract communities and their leaders in terms of the speed and power of receiving and diffusing shipping orders. In addition, we present a profit maximization model that utilizes the information diffusion power of community leaders. The results show the model is able to allocate shipping orders to the most suitable drivers in the shortest possible time and with the highest profit. To demonstrate the performance of the developed algorithm, we present a numerical example. Finally, a case study is applied to solve the optimization problem. The results show that the optimized behavior of companies in allocating orders to drivers is based on their risk level, reputation, and the average number of their customers.
Journal Article
Winner Determination with Sustainable-Flexible Considerations Under Demand Uncertainty in Transportation Service Procurement Auctions
Sustainability and flexibility are two main factors not being investigated explicitly by existing winner determination literature. From a fourth party logistics (4PL) provider’s point of view, an innovative sustainable-flexible winner determination problem under uncertain demand is particularly studied in transportation services procurement auctions. Based on a multi-attribute decision-making method, a linear constraint related to each bidder’s sustainability score and flexibility score can be constructed, and then we integrate an outside option policy to formulate a two-stage stochastic sustainable-flexible winner determination model. Subsequently, we develop an approximation approach to solve the model based on the principles of dual decomposition Lagrangian relaxation and the sample average approximation. Using an established generator to obtain random instances, the effectiveness and applicability of this research could be verified by conducting numerical experiments. Also, managerial insights can be obtained to provide decision support for running an efficient sustainable-flexible logistics system by using a Chinese 4PL firm’s real data.
Journal Article
Driving sustainable transportation: insights and strategies for shared-rides services
The concept of sharing, enabled by emerging technologies, is playing an increasingly important role in contributing to a transformation toward more sustainable transportation. This study aimed to contribute to the growing body of literature on on-demand transportation services, with a particular emphasis on sharing or pooling a ride when using services such as transportation-network companies (TNCs) and microtransit. The study conducted a shared mobility survey of over 2,500 respondents from selected locales across Texas—ranging from large urban areas to small cities and rural areas. We analyzed the survey data in detail using extensive statistical analysis and inferential techniques and adopted an analysis approach toward implementation-oriented research to address the gap between theory and practice. Demographic, as well as geographic and built-environment, factors were found to play an important role in determining whether users will opt for a shared or pooled service and/or how they perceive these alternatives. The findings highlight the importance of improving safety and security, increasing awareness of the benefits of ride-sharing, and designing appropriate policy measures to promote sustainable mobility. We identified potential operational improvements, government policies, and employer programs to improve shared-ride services and encourage their use, such as reducing uncertainty in shared rides and minimizing inconvenience for passengers. A critical finding was the need to prioritize operational improvements in shared-ride trips over solely relying on financial incentives to induce behavior change. Enhanced public awareness and education were also determined to be crucial regardless of the nature of improvements, policies, or programs that are implemented.
Journal Article
Resilience of Specialized Transportation Systems for People with Disabilities Under Extreme Weather Conditions
Climate change is increasing the frequency of extreme weather events, posing critical challenges for the resilience of specialized transportation services (STSs) that provide essential mobility for people with disabilities. In the South Korean context, heatwaves, cold spells, and heavy rainfall are particularly relevant because they directly affect health risks, trip demand, and operational reliability, making them central stressors for evaluating STS resilience in Busan. This study examines STS resilience in Busan, South Korea, focusing on three weather stressors: heatwaves, cold spells, and heavy rainfall. Large-scale operational data from the STSs of Busan were analyzed using the 4R (robustness, rapidity, redundancy, and resourcefulness) framework to classify daily service performance into distinct profiles. The analysis revealed that heatwaves coincided with reduced trip demand and shorter waiting times, yet this apparent stability reflected demand suppression rather than genuine robustness. Heavy rainfall produced the most severe disruptions, with longer and more variable waiting times that exacerbated inequities across users. Cold spells were associated with rapid recovery and the preservation of critical trips, although the small number of cases limits broader interpretation. These findings indicate that resilience in STSs is not uniform but event-specific, offering policy insights for strengthening operational stability and promoting equity in accessible transport.
Journal Article
Impact of College Provided Transportation on the Absenteeism and Academic Performance of Engineering Students
Students use different modes of transport to go to college. While many transportation programs exist at different universities and many experts find these programs to have a positive impact, no studies have investigated the impact of such programs on the absenteeism and academic performance of college students. The main purpose of this study is to investigate the impact of a college provided transportation program on the absenteeism and performance of engineering students. Different types of data were collected from a sample of engineering students, including attendance records, grade point average (GPA), course grades, majors, and bus ridership information for two years. The findings suggest that there is a positive impact of providing a college transportation service to engineering students in the form of better attendance and higher GPA. The outcomes of this study can be used to evaluate similar programs in the future and can be used by public agencies and policymakers to make decisions on expanding investments in such programs.
Journal Article
Modeling Public Transportation Policy Using Macroscopic Social Media Data Mining
Transportation policies must be created by the government, especially in countries with high population expansion, transportation services are used more to meet daily necessities. Conventional surveys to gauge public opinion are costly and slow; social media offers a macro-level proxy that can complement official data. This study employs large-scale online data mining to build decision support for transportation policy. We collected 19,806 Indonesia-based Twitter posts referencing public transport, private transport, sustainable mobility, and electric vehicles. After preprocessing, we fine-tuned IndoRoBERTa for sentiment classification and applied Latent Dirichlet Allocation for topic modeling. The sentiment model achieved 81.17% accuracy, with precision, recall, and F1-scores all above 0.80. Positive discourse concentrated on private vehicles, public transit, multimodal travel, and environmentally responsible practices, with many users endorsing eco-friendly private cars. Negative discourse emphasized severe air pollution, frequently attributing risk to emissions from private automobiles in Jakarta. Translating these insights into policy, we propose expanding electric-vehicle charging infrastructure, implementing vehicle buy-back/retirement programs, establishing low-emission zones, and promoting biofuels. The results demonstrate that macroscopic social media analytics can surface actionable public preferences and pain points, enabling near-real-time monitoring to inform adaptive and equity-oriented transportation policies. This framework provides a scalable approach for governments in rapidly growing contexts to align service provision with community sentiment while advancing sustainability goals.
Journal Article