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The last-mile delivery challenges and complexity are increasing simultaneously with the e-commerce growth, the e-commerce growth constitutes a threat to urban transport, especially for big cities and developing countries. This paper examines the economic impact of crowd-shipping based on public transport collaborated with the automated parcel locker and the impact of traditional delivery, as two different methods of last-mile delivery under the same circumstances. This paper aims to assess crowd-shipping as an alternative solution for last-mile delivery compared to traditional delivery. Data has been simulated based on Cairo reality, one of the largest cities in the world. The mathematical models were formulated for the problem, where the first model objective function minimizes the costs of vehicle possession and vehicle kilometers traveled, while the second model objective function minimizes the costs of establishing lockers, vehicle possession, and vehicle kilometers traveled. A genetic algorithm has been used due to the complexity of the problem. The results show that the economic impact of the crowd-shipping delivery system is better than the traditional delivery system under the conditions of large-sized and medium-sized demand, while the economic impact of traditional delivery is better than the crowd-shipping delivery system under the conditions of small-sized demand. [ABSTRACT FROM AUTHOR]

This paper presents the design, implementation, and practical use of a specialized online data repository and an interactive visualization of a transportation dashboard. Specifically tailored to handle and interpret large-scale transportation data within the Qatari context, the combined platform serves as a comprehensive solution for managing extensive datasets, including GPS traces from taxis and e-scooters, which are examined as primary use-cases in this paper. The online data repository provides a centralized hub for efficient data storage and management of public transport including Mobility-as-a-Service (MaaS) data. Concurrently, the visualization dashboard fosters an intuitive, user-friendly interface for data exploration and analysis. Through real-world applications within Qatar's transportation ecosystem, we elucidate how these innovative developments can inform data-driven policy decisions in crucial areas such as infrastructure development, resource allocation, and safety measures. Ultimately, this study underscores the pivotal role of effective data management and advanced visualization in maximizing the potential of big data, providing valuable insights for urban mobility planning and enhancing the landscape of policy-making in Qatar and worldwide. [ABSTRACT FROM AUTHOR]

This paper introduces research aimed at developing and implementing an intelligent passenger counting system in public transportation. The goal is to enhance fare evasion control and reduce financial losses for transit operators by automatically monitoring the entering and exiting of passengers. Utilizing the advanced capabilities of YOLO and DeepSORT algorithms, the system demonstrates high accuracy in detecting and tracking passengers, even in congested scenarios. Practical experiments have highlighted several challenges, including real-time frame processing rates, single-board computer performance, and the accuracy of passenger counts. It was discovered that the selection and positioning of USB cameras significantly influence system efficiency. Notably, changing the IP cameras to USB cameras significantly improves the processing speeds. A dataset containing images was created to improve the YOLO model's accuracy in detecting passengers. This dataset was particularly relevant considering the winter season, where identifying passengers could be challenging due to their bulky outerwear. To upgrade the performance of the DeepSORT system, the YOLO model was also improved with the pre-designed dataset to focus solely on the heads. This trained model achieved a recognition accuracy of 91% during peak hours. During periods of lighter traffic, when there were fewer passengers, the accuracy increased further due to reduced interference and higher precision. To enhance real-time processing efficiency, this research incorporates the status of public transport doors as a key parameter. By optimizing the number of frames forwarded to the YOLO algorithm for processing, we ensure that the system maintains accuracy without compromising on speed. Future development will focus on incorporating depth-sensing cameras and developing advanced data analysis algorithms for real-time processing on single-board computers. This will further enhance the precision of passenger counts during high-traffic periods. Expected benefits of this project include more efficient transport route planning, better management of passenger flows, improved rider satisfaction, and more effective measures against fare evasion. [ABSTRACT FROM AUTHOR]

Curbside parking in a residential area may be induced by the presence of a public transport stop. Travelers may park their car near the stop and continue their trip to the city center by other means. This is called informal park-and-ride. The magnitude of the phenomenon is estimated by simulation. Parking demand is derived from the history of parking time (tickets) sold at vending machines. For each ticket, an activity location is determined by stochastic sampling from a buildings (facilities) database based on the position of the vending machine. The activity timing is derived from the parking duration specified by the ticket. Suitable parking spots for an activity are determined for the cases (i) drive-park-walk and (ii) drive-park-publicTransport-walk respectively. The generalized cost (based on money and travel duration) is determined for both options. The decision is sampled by means of a behavioural model. Several scenarios are considered and the results allow to evaluate the complaints issued by residents of a study area because microsimulation enables the generation of probability densities for parking occupation in such area in each scenario. This paper reports how the method is applied to a study area in Amsterdam. [ABSTRACT FROM AUTHOR]

Station-pair passenger flow forecast modeling is crucial for public transportation to address emerging needs. The accurate prediction and estimation will provide backbone support for various features of transport, viz. (a) Demand forecasting for resource allocation during peak travel periods; (b) Service planning to determine transportation frequency on specific routes; (c) Resource allocation for vehicles and capacity adjustments, including planning for new fleet size; (d) Infrastructure planning to identify areas requiring additional development; (e) Enhancing passenger experience with ample seating and amenities; and (f) Emergency planning for crowd control and passenger safety. Recent advancements in transit information station-pair estimation methods can be implemented with Data Science. Over the years, there has been significant progress in developing innovative modeling approaches to estimate travel behaviour, taking into account various factors such as timeslots, holidays, and other relevant features. In this study, we have synthesized ticket data from Thane Municipal Transport (TMT), collected from e-ticket machines, spanning from January 1 to June 23, 2023, covering all routes and comprising more than 2.80 crore tickets. This paper presents a comprehensive station-pair short-term passenger flow forecast by utilizing three boosting models: extreme Gradient Boosting (XGBoost), Light Gradient Boosting model (LightGBM), and Categorical Boosting model (CatBoost). Their implementation is well-suited based on statistical parameters. The study concludes by comparing the results of the models, considering computation time and statistical parameters, to validate their superiority. By looking into day to day requirements, the LightGBM model is most appropriate for implementation with an accurate result. The research findings contribute to providing valuable data support and a robust scientific foundation for managing passenger flow through route planning and dispatching in urban transit systems. [ABSTRACT FROM AUTHOR]

Integration of bike usage and transit services is an effective way to enhance accessibility for both transportation modes. Using high-resolution transit data, the study analyzes bike-transit multimodal accessibility and usage patterns through a social equity lens. Two types of accessibility increment are studied: bicycle increment to public transit – the benefits of using bicycle for transit riders, and transit increment to cycling – the merits of using transit for cyclists. Results show that bike-transit integration benefits both public transit riders and cyclists, expanding their accessible opportunities by up to 70% and enabling longer trips for cyclists while providing continuous benefits for public transit users. Meanwhile, better infrastructure significantly improves multimodal accessibility, resulting in more increment for public transit riders but less increment for cyclists. The paper also shows the spatiotemporal patterns of multimodal ridership. The research highlights disparities in bike-transit activities for Black communities due to inadequate local biking infrastructure. Black people majority neighborhoods enjoy less increment compared to other neighborhoods for shorter and very long trips; they also have disproportionately lower multimodal ridership despite much higher transit ridership and better transit access. Enhancing biking infrastructure in these areas can improve physical accessibility increment and promote social equity. The paper provides practical insights for transit planning, emphasizing the importance of connecting bike lanes and creating safer streets for cycling. [ABSTRACT FROM AUTHOR]

Improving energy efficiency in public passenger transport is key to achieving sustainability goals. In many developing countries, public passenger transport relies heavily on fossil fuels, which necessitates a strong focus on reducing emissions from energy consumption. Surprisingly, few studies have addressed this issue in the context of daily operations, with most research focusing on operational and infrastructural changes. This paper aims to fill this gap by introducing detailed energy consumption variables into a public transport simulation model using SUMO, a tool known for its seamless integration with Python. The development of two Python scripts underpins this endeavour. The first script facilitates the automatic generation of scenarios, allowing the manipulation of parameters such as station spacing, line length, passenger capacity and waiting times, among others. The second script is dedicated to estimating energy consumption for each scenario. Together, these tools allow us to calculate average energy consumption for different scenarios and modes of public transport. As a result, they provide valuable insights for the formulation of public policies aimed at saving energy in different public transport environments and lay the foundation for the integration and development of intelligent transport systems and simulation-based digital twins. [ABSTRACT FROM AUTHOR]

In smart cities, biometric technologies have become extensively used for ticket authentication on public transport. Information fusion plays a key role in biometric ticketing, allowing ticket validation with more data source validation in different public transport modes. This paper proposes a novel biometric technology-based mobile ticket application-based system. We formulate the problem as a multi-agent reinforcement learning framework for biometric ticketing in multi-transport environments. Specifically, we propose the Asynchronous Advantage Critic Biometric Ticketing Framework (A3CBTF) algorithm, which consists of different schemes based on the proposed system. The proposed algorithm framework operates in hybrid transport modes using a parallel reinforcement learning scheme. A key advantage of A3CBTF is that it enables passengers to use a single ticket for various public transport modes. Additionally, even when a passenger's mobile device is stolen, lost, or has a dead battery, they can still validate their tickets through different information fusion sources, such as fingerprint and face recognition. A3CBTF is a multi-agent system that integrates mobile, transport, edge, and cloud servers to facilitate ticket validation in a distributed environment. By optimizing both convex and concave optimizations, A3CBTF ensures efficient ticket validation with minimal processing time and maximizes validation rewards across different biometric technologies. Experimental results demonstrate that A3CBTF outperforms mobile off with other options such as fingerprint and face recognition in public transport as compared to other ticketing systems. • A novel biometric technology-based mobile ticket application. • Reinforcement learning: Multi-agent solution for diverse transport. • A3CBTF framework: Parallel learning in hybrid transport modes. • Unified ticketing: A3CBTF enables single-ticket use across public transport. • Integrated validation: A3CBTF unifies mobile, transport, edge, and cloud servers. [ABSTRACT FROM AUTHOR]

With the continuous evolution and refinement of urban functional spaces, the escalating reliance of commuters on public transportation for work-related travel has surged with time. This paper offers a multidimensional overview of research pertaining to identifying, analyzing, and forecasting of urban public transportation commuting patterns. Initially, emphasis is placed on the identification of commuting patterns. We delineate the characteristics of four data sources, summarize the establishing of travel chains, and classify the approaches involved in identifying commuting patterns. Subsequent to this, we delve into the type of determinants influencing commuters, exploring the ramifications of spatial–temporal heterogeneities. Thereafter, we classify and expound upon forecasting methodologies for commuter flow, elucidating principles behind predictive algorithms and composite models grounded in statistical and time-series analyses, discrete choice modeling, conventional machine learning, and advanced deep learning techniques. Concurrently, a comparative assessment of the strengths, limitations, and applicability of each method is presented. Concluding our exposition, predicated on current research landscapes and inherent challenges, prospective trajectories for future exploration are proposed, including multi-source data fusion, real-time commuting pattern detection, precise public transit for commuting, and commuting travel under shared transportation. [ABSTRACT FROM AUTHOR]

Public transit offers urban populations physical accessibility to resources and opportunities. However, at the same time, transit trips often expose users to extreme environmental conditions, such as extreme heat and cold since transit journeys usually include out-of-vehicle trip segments including walking and waiting. Such exposure can be considered as environmental health costs because exposure to weather extremes can lead to adverse health outcomes. Even worse, climate change is increasing the intensity and frequency of extreme weather events. In this context, how can we make public transit accessibility measures ready for climate change? This paper attempts to answer this question by developing a generalized cost function approach combining travel time and environmental health costs into an integrated measure of dual accessibility: a measure of the travel costs of accessing a fixed number of destinations. We synthesize transport science, environmental health, remote sensing, and urban climatology to empower the proposed framework. To demonstrate the utility of the proposed method, we carry out an example study that incorporates transit passengers' extreme cold exposure into accessibility measures in the city of Winnipeg, Manitoba, Canada. Further, we perform a social equity analysis to investigate whether the increase in total integrated costs (i.e., decrease in accessibility) due to the inclusion of environmental health costs disproportionately affects socially disadvantaged population groups. The proposed method enables a more realistic and practical measurement of public transit accessibility under climate change; thereby, improving the readiness and resilience of our society and transport systems for future challenges. [ABSTRACT FROM AUTHOR]

Most choice models, e.g. Multinomial Logit (MNL), rely on random utility theory, which assumes that a compensatory utility maximization decision rule explains an individual's choice behaviour. Research has shown, however, that behaviour is sometimes better explained by non-compensatory decision rules. While some research has used Latent Class Choice Models (LCCMs) to account for multiple decision rules, many of them – such as the disjunctive rule – have yet to be explored. This paper formulates, estimates, and evaluates a LCCM that combines the MNL with a Generalised Random Disjunctive Model (GRDM), a new choice model we develop. Addressing deficiencies of existing disjunctive choice models, the GRDM allows for relative importance between attributes and is insensitive to irrelevant attributes. Unlike most non-compensatory models, it is tractable and incorporates random error terms for capturing unobserved heterogeneity across choice situations. The GRDM can be expressed as a Universal Logit (UL) model, which helps derive welfare metrics such as Marginal Rates of Substitution and elasticities and makes it possible to estimate the model with traditional software packages. The LCCM combining the GRDM and the MNL is estimated in two large-scale case studies: cyclists' route choice and public transport route choice. Results are compared with other relevant LCCM specifications and the individual choice models, where it is found that the MNL + GRDM LCCM provides the best fit to the data. We also interpret the fitted parameters and calculate the Marginal Rates of Substitution, which align with behavioural expectations. • Formulation of the new Generalised Random Disjunctive Model (GRDM). • Derivation and interpretation of the GRDM Marginal Rates of Substitution and Logsum. • Motivation for accounting for decision rule heterogeneity in choice models. • Presentation of a model combining the GRDM and a MNL, using a Latent Class framework. • Benchmark and interpretation of the new models on two route choice case studies. [ABSTRACT FROM AUTHOR]

• Thoroughly reviewed existing literature on digital twins and digital shadows for SHM-based maintenance in advanced rail transit systems. • Highlighted the trends, critical applications and knowledge gaps regarding DTs and DSs in SHM. • Discussed core requirements of DTs and differentiated them from other similar digital technologies. • Developed and presented a conceptual framework for developing SHM-DTs in advanced rail transit systems. • Presented a research prospectus for the future application of DTs in the rail transit industry, considering identified barriers to implementation and adoption. Advanced rail transit systems (ARTS), including high-speed rail and maglev trains, provide enhanced transportation options to meet the growing demand for efficient transportation systems. However, they present unique challenges in maintaining the safety and performance of their infrastructures. Structural health monitoring (SHM) has emerged as an essential practice to forestall the potential consequences of structural defects in ARTS. Recently, digital twins and digital shadows have been successfully employed in various industries to monitor the state of physical systems. However, their application for structural health monitoring in ARTS remains largely unexplored. Hence, this article explores the potential of digital twins and digital shadows, in improving structural health monitoring in ARTS. Due to the digital twins' ability to bi-directional communication between a real system and its virtual replica, this article presents a comprehensive literature survey on their enablers and capabilities. Meanwhile, a framework for digital twins-based monitoring in ARTS is also proposed. The key distinctions and benefits of digital twins over other Industry 4.0 digital representation concepts, such as real-time monitoring, optimization, prediction, simulation, and decision-making, are identified. The paper highlights the significant opportunities that digital twins, especially, can offer to improve health monitoring. Similarly, limitations and bottlenecks that must be tackled in future research for implementations are also acknowledged. Finally, harnessing the power of digital twins can catalyze a transformative shift in ARTS, leading to more effective monitoring, enhanced safety, and improved performance. [ABSTRACT FROM AUTHOR]

In this paper, we employed Data Envelopment Analysis (DEA) to assess how efficiently a sample of 22 European cities meets the SDG 11.2.1 indicator, which measures the proportion of the population with convenient access to public transport. We examined a range of cities across different sizes and regions. After using bootstrapping methods, we found that the public transport systems in southern European cities arrange their stop networks more efficiently to provide convenient access for inhabitants than those in northern cities. To reach the DEA performance benchmark, the low-efficient systems would need to reorganize their stop network and increase the population's access to public transport by an average of 58%. However, since almost all users of these systems can already walk acceptable distances to stops, these inherent inefficiencies are mainly observed due to unfavorable built environment conditions, which require a relatively extensive network of road and rail public transport stops. We estimated conditional order-m measures to unravel such exogenous influence on public transport performance. After analyzing various built environment factors, we found that cities with increased population density and a higher proportion of urbanized and built-up areas facilitate more convenient access to public transport. Cities that prioritize car-centric designs tend to have less favorable accessibility conditions. Our findings are useful for policymakers in achieving UN SDG 11 in modern cities. Expanding public transport stop networks and rail modes is important in low-density and less urbanized districts. Urban planning that promotes a more pedestrian-focused design can also improve public transport accessibility. • Spatial efficiency DEA models evaluate SDG 11.2.1 progress in 22 European cities. • Public transport is easily accessed but often inefficient, with excessive stops. • Conditional nonparametric analysis explained built environment impact on efficiency. • Denser and more urbanized cities achieve the SDG 11.2.1 goals more easily. • Diverse and less car-related land uses reduce walking distances to public transport. [ABSTRACT FROM AUTHOR]

Subgroup discovery (SD) aims at finding significant subgroups of a given population of individuals characterized by statistically unusual properties of interest. SD on event logs provides insight into particular behaviors of processes, which may be a valuable complement to the traditional process analysis techniques, especially for low-structured processes. This paper proposes a scalable and efficient method to search significant SD rules on frequent sequences of events, exploiting their multidimensional nature. With this method, it is intended to identify significant subsequences of events where the distribution of values of some target aspect is significantly different than the same distribution for the entire event log. A publicly available real-life event log of a Dutch hospital is used as a running example to demonstrate the applicability of our method. The proposed approach was applied on a real-life case study based on the public transport of a medium size European city (Porto, Portugal), for which the event data consists of 133 million smartcard travel validations from buses, trams and trains. The results include a characterization of mobility flows over multiple aspects, as well as the identification of unexpected behaviors in the flow of commuters (public transport). The generated knowledge provided a useful insight into the behavior of travelers, which can be applied at operational, tactical and strategic business levels, enhancing the current view of the transport services to transport authorities and operators. • Significant subgroup discovery rules on sequences of multidimensional events. • Process discovery and conformance checking on low-structured processes. • Real-life case study based on smartcard travel validations of a transport network. • Characterization of mobility flows over multiple aspects. • Identification of unexpected behaviors in the flow of commuters. [ABSTRACT FROM AUTHOR]

This paper examines the economic and policy implications of the charging, network and auxiliary infrastructure required for a fully electrified metropolitan Melbourne bus network using overnight depot charging. We introduce the concept of minimum and maximum fleet charging capacities, as well as a charging infrastructure augmented total cost of ownership that is faced by bus fleet owners. Our analysis provides policy makers with a range of viable fleet charging capacities and a cost component breakdown of public bus fleet electrification using battery electric vehicles. We find that a minimum of half the fleet's rated charging capacity is needed to maintain operation, which provides a lower bound for network and charging infrastructure costs. Considering the augmented total cost of ownership (per km), charging, network and auxiliary infrastructure costs contribute 10–19%. A gradual transition to battery electric buses over 12 years yields further TCO savings of between 4.6 and 5%. We recommend streamlining the network augmentation process to reduce uncertainty and project delays. • Calculation of viable charging capacities to operate a metro BEV bus fleet • BEV buses operate using an overnight charging strategy with real world schedules • Fleet can continue operating with half its rated charging capacity • TCO is augmented with charging and network infrastructure costs • Charging, network and auxiliary infrastructure contributes 10–19% to the TCO [ABSTRACT FROM AUTHOR]

Modern technologies provide new opportunities for the industry but raise the expectations of the customers as well. This is valid also for the postal, courier, and logistics industries. The biggest challenge for the companies in the field is to improve the efficiency of the final phase in the transfer of shipments – last-mile delivery. A contemporary solution relates to collaboration between delivery companies and citizens who are willing to perform the delivery tasks as an additional activity to their established job or routine. Such a concept is known as crowdsourcing. There are several approaches within crowdsourcing, for example, transportation by car, utilizing public transport, etc. To make an appropriate choice, multiple criteria should be considered. This paper aims to propose an original methodology for solving the explained multi-criteria decision-making problem. The proposed framework is based on the application of the Sugeno–Weber nonlinear functions in a fuzzy environment. The application of the fuzzy Sugeno–Weber weighted assessment methodology showed that the proposed methodology has adaptability, a high degree of generalization, and stability of results. This enables the applicability of the methodology to various tasks from a broad spectrum of areas, which represents an additional value. A real-life case study is provided to solve the prioritization of crowdsourcing models in suburban municipalities of Belgrade, Serbia. The available alternatives and the most important criteria for the crowdsourced delivery model are identified. All criteria are divided into four groups, which define the dimensions of sustainability, including the technical aspect. The results of the conducted research indicate a high level of convenience in using one's car or daily bus lines for crowdsourced delivery to optimize the entire process of shipment transfer in the observed territory. [ABSTRACT FROM AUTHOR]

With the global acceleration of the carbon-neutral process and the widespread adoption of electric buses in various countries, the placement of charging stations plays a crucial role in promoting low-carbon development in public transportation systems. This paper proposes a two-stage approach for siting electric bus charging stations considering future-present needs. Initially, perspectives on future and current needs, a community discovery model is constructed using bus space-time swipe card data to identify the municipal bus travel link network. This model considers the OD distribution of bus passenger flow, which is essential for determining the line network and capacity layout. Additionally, a bus first and last station hotspot identification-Voronoi diagram model is established based on the service area partition of bus charging stations, incorporating the weight of charging demand. The model assesses the aggregation of charging demand in each service area using demand aggregation and spatial aggregation indicators. It utilizes the current bus charging requirements aggregation pattern to determine the siting of bus charging stations in the sub-regions, resulting in a data-driven two-stage method for siting electric bus charging stations considering future and current demand. The city is divided into 11 bus charging station subzones using the community discovery model, and sites are selected within each subzone. The results indicate that the demand aggregation degree has increased to some extent in 10 service areas compared to the initial sub-district and an overall increase of 22.79% for the entire city, from 0.487 to 0.598. Among them, 8 service areas have a demand aggregation degree greater than 0.5, indicating that charging demand in most sub-districts is concentrated in high-value aggregation areas. The spatial aggregation degree of all service areas is less than 1, with a decrease in the spatial aggregation degree of 7 service areas and an overall decrease of 11.47% for the entire city, from 0.680 to 0.602. Most service areas experience a decrease in spatial aggregation degree while the demand aggregation degree increases, optimizing the structure of the service areas and reducing the time cost of charging the buses. The model demonstrates good applicability to charging stations and offers an effective approach to siting them. • Use of residents' travel data to delineate initial service areas for charging stations. • Consider the longevity of charging station use in terms of the stability of the spatial distribution feature of travel OD. • Introducing demand aggregation and spatial aggregation to measure the aggregation structure of charging demand. • Site selection based on the distribution of charging demand hotspots at bus route terminals. • Considered the implementability of bus charging station deployment. [ABSTRACT FROM AUTHOR]