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In recent years, all-inorganic perovskite materials have become an ideal choice for new thin film solar cells due to their excellent photophysical properties and have become a research hotspot. Studying the ultrafast dynamics of photo-generated carriers is of great significance for further improving the performance of such devices. In this work, we focus on the transient dynamic process of CsPbBr3/TiO2 composite systems with different Mn2+ doping contents using femtosecond transient absorption spectroscopy technology. We used singular value decomposition and global fitting to analyze the transient absorption spectra and obtained three components, which are classified as hot carrier cooling, charge transfer, and charge recombination processes, respectively. We found that the doping concentration of Mn2+ has an impact on all three processes. We think that the following two factors are responsible: one is the density of defect states and the other is the bandgap width of perovskite. As the concentration of doped Mn2+ increases, the charge transfer time constant shows a trend of initially increasing, followed by a subsequent decrease, reaching a turning point. This indicates that an appropriate amount of Mn2+ doping can effectively improve the photoelectric performance of solar cell systems. We proposed a possible charge transfer mechanism model and further elucidated the microscopic mechanism of the effect of Mn2+ doping on the interface charge transfer process of the CsPbBr3/TiO2 solar cell system. [ABSTRACT FROM AUTHOR]

This research is intended to support the implementation of a program led by the Southern California Association of Governments (SCAG) to support new transportation-related pilot projects in the region through 2026. Although pilot projects have become popular in transportation planning and other policy sectors, often to test new technologies or ideas before fully committing to them, agencies are struggling to turn their successful pilots into permanent programs. To further analyze the use of transportation pilots in the U.S., 15 interviews with practitioners were conducted and seven case studies of specific projects were analyzed. Findings revealed many shared experiences across agencies, despite their unique circumstances. Identifying long-term funding and adequate staff capacity were some of the most commonly cited challenges, but pilots can also come up against unexpected external barriers. Pilot-operating staff must be adaptable and willing to make significant adjustments or give up on a project when necessary. Also crucial are the method and scale of pilot evaluation, communication strategies with the public before and during the pilot, and partnerships with local organizations or universities. The recommendations provided for SCAG and other agencies are organized into five categories: long-term planning, goals and evaluation, partnerships, community involvement, and decision-making. This research contributes to a growing interest in the use of pilot projects for streamlining government processes and promoting risk-taking and innovation in public agencies.

This project is a mixed-methods case study of a completed freeway-to-boulevard project in Rochester, New York. To spatially assess potential benefits to quality of life, I compared the years before and after the project was completed in the area surrounding the Inner Loop East using a combination of census data, air quality estimates, asthma ED-visit rate data, traffic volume data, and bicycle/pedestrian counts. I also interviewed planners, community advocates, and residents about the project. Evidence from the interviews was supplemented with document analysis of news coverage, social media commentary, public meeting notes, and city planning documents. Through this case study, I found that the Rochester Inner Loop East Transformation project succeeded in improving mobility and connectivity and creation of new affordable housing units, but that it likely also contributed to gentrification in the area and displacement of Black and low-income residents. I identified key lessons for future projects to address air quality, traffic, displacement, and community engagement, which aim to inform the EPA Office of Community Revitalization as they begin community engagement and design processes for similar projects in other communities.

We ask how place-based transportation plans are being evaluated, and what insights from the broader policy and plan evaluation research literature might inform evaluation design. We complement a review of the evaluation literature with six expert interviews with 15 people. We find that California agencies and their community partners have high expectations for evaluations of place-based transportation plans. So far, however, those evaluations have been less successful in providing detailed information on outcomes and the causal impact of interventions. This does not reflect the shortcomings of the evaluation teams, but rather the inherent challenges in holistically assessing a diverse set of projects on different implementation timelines in a project area with porous boundaries. There is also a fundamental difficulty with the evaluation scale. California’s place-based transportation plans have often been evaluated individually. But in general, evaluations, particularly quantitative causal inference methods, are most effective with a larger number of projects or sites. We suggest a two-pronged approach to addressing the tensions that we identify between place-specific knowledge and generalizable conclusions. The first prong, at the site level, would emphasize process evaluations and assessment of outputs and outcomes. The second prong would focus on impacts across multiple sites and the extent to which place-based transportation programs have a causal role.

This study discusses the potential economic and development impacts that high-speed rail (HSR) may bring to California. The research reviews the reported impacts of HSR implementation in various countries, particularly in Europe, and case studies of selected HSR station-cities in France, Spain, and Italy. The analysis suggests that HSR could bring economic development to the state and stimulate population growth but might eventually lead to gentrification in certainlocations. Not all station-cities experience the same impacts, and certain conditions may foster greater economic development. Station location and connectivity to downtown areas would be particularly important in influencing these impacts, while peripheral stations would be less able to attract land use development and relocation of activities. The availability of rail service to larger cities (and connections to other major markets) and the coordination with urban planning and policy are key to determining the development of areas around HSR stations. The study indicates that for HSR to bring about desired economic development, the planning and design of stations and services must be integrated with the vision and urban plans of each station-city.

With the explosion of e-commerce, optimising parcel transportation has become increasingly important. We study the long-haul stage of parcel transportation which takes place between sorting centres and delivery depots and is performed on a two-level hierarchical network. In our case study, we describe the application framework of this industrial problem faced by a French postal company: There are two vehicle types that must be balanced over the network on a daily basis, and there are two possible sorting points for each parcel, which allows a better consolidation of parcels. These industrial constraints are formalised in the Long-Haul Parcel Transportation Problem (LHPTP). We present a Mixed Integer Linear Program (MILP) and a hierarchical algorithm with aggregation of demands which uses the MILP as a subroutine. We perform numerical experiments on large-size datasets provided by a postal company, which consist of approximately 2500 demands on a network of 225 sites. These tests enable the tuning of certain parameters resulting in a tailored heuristic for the LHPTP. Our algorithm can serve as a decision aid tool for transportation managers to build daily transportation plans, modeled on solutions produced given daily demand forecasts and can also be used to improve the network design. [ABSTRACT FROM AUTHOR]

Street networks allow people and goods to move through cities, but they are vulnerable to disasters like floods, earthquakes, and terrorist attacks. Well-planned network design can make a city more resilient and robust to such disruptions, but we still know little about worldwide patterns of vulnerability, or worldwide empirical relationships between specific design characteristics and resilience. This study quantifies and measures the vulnerability of the street networks of every urban area in the world then models the relationships between vulnerability and street network design characteristics. To do so, we simulate over 2.4 billion trips across more than 8,000 urban areas in 178 countries, while also simulating network disruption events representing floods, earthquakes, and targeted attacks. We find that disrupting high-centrality nodes severely impacts network function. All else equal, networks with higher connectivity, fewer chokepoints, or less circuity are less vulnerable to disruption's impacts. This study thus contributes a new global understanding of network design and vulnerability to the literature. We argue that these design characteristics offer high leverage points for street network resilience and robustness that planners should emphasize when designing or retrofitting urban networks.

Commuting flows refer to the regular movement of people from their homes to workplaces. The spatial heterogeneity of commuting flows indicates the uneven spatial distribution of places of residence, employment, or job-residence connections. Understanding the characteristics of spatial heterogeneity in commuting flows is vital for effective transportation planning. However, limited by the scarcity of flow data, traditional research on commuting flows predominantly focuses on the spatial distribution of employment, residence, and job-residence connections individually, which fails to unveil the spatial heterogeneity of commuting flows. In this study, we examined the spatial heterogeneity of commuting flows in Beijing using mobile phone data. We analyzed the degree of heterogeneity and the aggregation scale of commuting flows. The results showed that (1) the degree of spatial heterogeneity varies between different regional pairs, and (2) the aggregation scale of commuting flows varies with distance; moreover, the longer the distance, the larger the aggregation scale of commuting flow distribution, and dominant heterogeneous clusters expand from the center to the periphery. These findings enhance the strategic planning of public transit. In less densely populated areas such as those outside the Fifth Ring Road, efficiency can be boosted by new bus routes. In contrast, denser areas such as those within the Third Ring Road, may benefit from an integrated approach: establishing new rail systems alongside expanded bus services to optimize commuting. [ABSTRACT FROM AUTHOR]

The demands of on-orbit transporting large structures via multiple space robots are emerging in space applications to increase the limited transportation capability of single space robots. However, the high level of system coupling arising from the increased number of individuals and physical interaction between structures and robots brings great challenges to the consequent control. This paper focuses on the motion planning of collaboratively transporting a large space structure using rigidly connected space robots. First, the system model is established at a kinematic level with non-conserved momenta due to the effects of thrusters on the bases of robots. Then, a cooperative control model is formulated to minimize the fuel consumption by balancing the thrusts and torques respectively acting on the bases and redundant arm joints of space robots. The physical limitations of space robots and mission related constraints such as joint angle limits, control input saturation and collision avoidance for multiple space obstacles are also taken into account in the optimization. Afterwards, the effectiveness of the proposed control scheme is validated and demonstrated by numerical case studies. [ABSTRACT FROM AUTHOR]

AbstractBikeshare system operators have leveraged online platforms to gather user feedback on station experiences and service quality. This crowdsourced data presents a valuable opportunity to enhance bikeshare planning and operations. However, planners have limited methods to analyze those qualitative data given the sparse nature of this data, which is insufficient for training comprehensive machine learning models. Addressing this challenge, our study employs a novel approach combining spatial analysis and Factor Analysis of Mixed Data (FAMD) to delve into bikeshare users’ perceptions and expectations. Focusing on Chicago’s bikeshare system, we utilize its limited online comments to demonstrate the robustness of our algorithm and its applicability to both large and small datasets. Our findings reveal a geographic pattern in feedback: negative comments on bike rebalancing, station locations, and facilities are concentrated in the city center, while dissatisfaction with the cycling environment is consistent across both urban and peripheral areas. Moreover, we discovered that the demographic and employment characteristics of areas surrounding bikeshare stations significantly influence positive feedback, overshadowing the impact of station design and local infrastructure. Overall, this study offers a quantitative framework for leveraging limited crowdsourced feedback effectively, providing strategic insights for refining bikeshare planning and operational decisions. [ABSTRACT FROM AUTHOR]

This study looks at how travelers move between MADINA and JEDDA, using the UPPAAL Stratego tool to tackle the complexities of urban mobility. As cities grow, effective transportation planning becomes more challenging. Travelers have three options: car, bus, and train. The choices for car and bus travel are impacted by traffic conditions, which can vary between heavy and light, affecting both travel time and cost. We propose a detailed mathematical model that captures all possible scenarios related to these travel options, incorporating the uncertainties of real life. This allows us to simulate different traffic situations. By using UPPAAL Stratego, we evaluate three strategies: the Safe Strategy, which minimizes risk; the Fast Strategy, which aims to reduce travel time; and the Fast and Safe Strategy, which seeks a balance between speed and safety. This paper starts with an introduction to the Stochastic Priced Timed Games approach, highlighting its relevance in modeling dynamic travel environments. We then provide an overview of UPPAAL Stratego, showcasing its abilities in generating, optimizing, and comparing strategies. Next, we outline our mathematical model, explaining the assumptions, parameters, and data sources we used. Our simulation results illustrate how each strategy performs under different conditions, shedding light on traveler preferences and behaviors. The findings underscore the significance of accounting for traffic variability in travel planning and offer important insights for urban transportation policies aimed at improving the traveler experience and optimizing resource use. Additionally, we emphasize the theoretical contributions of our model by demonstrating its applicability to real-world scenarios and its potential to inform future research in urban mobility optimization. Ultimately, this research adds to the growing knowledge of smart transportation systems, demonstrating how formal mathematical modeling can address complex real-world challenges and inform future urban mobility strategies. [ABSTRACT FROM AUTHOR]

The proliferation of mobile phones has generated vast quantities of cellular signaling data (CSD), covering extensive spatial areas and populations. These data, containing spatiotemporal information, can be employed to identify and analyze intercity transport modes, providing valuable insights for understanding travel distribution and behavior. However, CSD are primarily intended for communication purposes and are not directly suitable for transportation research due to issues such as low spatial precision, sparse sampling granularity, and lacking traffic semantic features. This article proposes a Hybrid model for identifying individual intercity transport modes based on CSD. Several multidimensional mobility features are proposed that extract interpretable motion characteristics from CSD. A preliminary transport mode probability judgment is made based on the mobility features. Then, the complete transport mode is confirmed considering the temporal continuity correlation of the entire trace. Experiments confirm the Hybrid model's superior precision in identifying transport modes over baseline models, with an average F1 score of 0.92, maintaining high accuracy across various trajectory lengths. This model would support further studying individual intercity travel behavior patterns, aiding transportation planning and operational management decisions using CSD. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive approach for planning for teams of heterogeneous robots with different capabilities and the transportation of resources. We use Capability Temporal Logic (CaTL), a formal language that helps express tasks involving robots with multiple capabilities with spatial, temporal, and logical constraints. We extend CaTL to also capture resource constraints, where resources can be divisible and indivisible, for instance, sand and bricks, respectively. Robots transport resources using various storage types, such as uniform (shared storage among resources) and compartmental (individual storage per resource). Robots' resource transportation capacity is defined based on resource type and robot class. Robot and resource dynamics and the CaTL mission are jointly encoded in a Mixed Integer Linear Programming (MILP), which maximizes disjoint robot and resource robustness while minimizing spurious movement of both. We propose a multi-robustness approach for Multi-Class Signal Temporal Logic (mcSTL), allowing for generalized quantitative semantics across multiple predicate classes. Thus, we compute availability robustness scores for robots and resources separately. Finally, we conduct multiple experiments demonstrating functionality and time performance by varying resources and storage types. [ABSTRACT FROM AUTHOR]

Ports are severely challenged by the uncertainty of the transportation environment. To evaluate the stability of maritime transportation network in Northeast Asia under port disruption, a network stability analysis model based on node failure rate that considers static and structural, dynamic and functional is proposed in this paper. In this model, the stability of the maritime transportation network is redefined. Based on complex network theory, the statistical topological parameters and evolution of the network are quantitatively analyzed. The active response and passive adaptation process of the maritime transportation network to the failure rate of the port are analyzed. The evaluation results illustrate that the initial stability of the port is proportional to the non-redundant connections and independence of the port. Network location advantages profoundly affect the probability of port failures. The diffusion of port failures in maritime transportation is phased, and the network is adaptive to port disruptions and has high stability. These results contribute to better planning of maritime transportation routes and strategies for port calls by strategic planners of shipping companies. • Node pressure is introduced to quantify the probability of node failure. • A network stability model that combines static and structural, dynamic and functional. • The number of non-redundant links in the port determines its initial stability. • The port's network location advantage is correlated with the port's failure probability. • The impact of failures on marine transportation is phased [ABSTRACT FROM AUTHOR]

Mobility as a Service (MaaS) has become an emerging trend in transportation planning and management due to its potential to overcome the challenges of urban mobility toward the achievement of sustainable mobility goals. This requires people to be willing to use MaaS apps. However, given their relative novelty, there is still a lack of research on the factors that influence the use of these apps, as well as the possible role of gender in such use. In this context, this study aims to explore the complex relationships between a set of relevant factors and their influence on the intention to adopt MaaS apps, with special attention to the role of gender as a moderating variable. For this purpose, a survey was conducted in the Madrid Metropolitan Area (Spain), comprising 8358 respondents (3627 men and 4731 women), and a four-step methodological procedure was adopted: (i) Exploratory Factor Analysis (EFA), (ii) Confirmatory Factor Analysis (CFA), (iii) Structural Equation Model (SEM) on the total sample (iv) Gender-sensitive analysis, including a Multi-group analysis – SEM. The results demonstrate that technophilia, perceived usefulness and perceived ease of use are determining factors. Green values and attitudes toward shared modes have a notable total positive effect. However, contrary to expected, attitudes toward public transport, car and bicycle hardly explain the intention to adopt MaaS apps. Our analysis also reveals significant gender differences in the causal relationships hypothesized in the model. These findings allow us to outline the policy initiatives that could favor the use of MaaS apps, with a gender-sensitive approach. • Development and validation of a structural model on the intention to use MaaS apps. • Technophilia, perceived usefulness and perceived ease of use are crucial factors. • Green values and attitudes toward shared modes have a notable total positive effect. • Attitudes toward public transport, car and bicycle hardly explain MaaS apps usage. • Gender plays a moderating role in the structural model proposed. [ABSTRACT FROM AUTHOR]

Traditional transportation planning emphasized the direct economic benefits of transportation infrastructure. While there is a growing awareness of the environmental consequences, social impacts are frequently overlooked due to methodological complexities and political neglect. Although existing literature acknowledges the existence of transportation-related social exclusion, it primarily concentrates on quantifiable and immediate short-term social outcomes arising from improved accessibility and affordability, which facilitate place-to-place interactions and activities. There has been limited exploration of the more intricate and indirect societal impacts associated with transportation. This paper argues that new transportation infrastructure not only affects mobility and travel experiences but also has deeper social impacts on the sense of place. These effects may extend to broader benefits, including academic performance, by influencing place identity, attachment, and dependency, which in turn affect daily activities unrelated to travel. Our study focuses on a university student community (N = 323), a pivotal future workforce, and investigates how the introduction of a new metro line passing through the campus reduces commuting time, subsequently enhancing academic productivity. Employing structural equation modeling method, our analysis reveals that sense of place acts as a mediator between travel experience and student engagement under the influence of transportation mode choice. These findings may have broad applicability in identifying indirect social impacts of transportation infrastructure, thus providing insights for investment decisions. • Place-centered approach for assessing social impact of transport infrastructure. • Focus on a student community as non-working population but future workforce. • Investigate deeper/wider impact to/from sense of place on academic productivity. • Case study of a recent metro extension project, Shatin to Central Link, in Hong Kong. • Switching from bus to train enhances SoP and academic performance. [ABSTRACT FROM AUTHOR]

Reliability is a key factor in public transportation systems, significantly influencing passenger satisfaction and their perceptions of service quality. Therefore, measures of Travel Time Reliability (TTR), which help quantify unexpected delays, are essential for effectively planning and managing travel times. This research aims to provide a prediction model for standard deviation of travel time, as an indicator of TTR, on an urban bus route using Automatic Vehicle Location data as well as evaluating and comparing the results of ARIMA and LSTM models. Our research contributes to the existing body Ofliterature by considering the effect of a wide range of predictor variables which were categorized by route characteristics, weather condition, congestion, and temporal feature, on bus TTR, which received less attention in previous studies. The results show that LSTM is an efficient and accurate TTR predictive model than ARIMA, with an accuracy of about 87%. In addition, the LSTM model exhibits lower mean absolute percentage error (40%), mean square error (52.08%), and root mean square error (39.70%) compared to the corresponding values in the ARIMA model. Also, the congestion, weather condition, and holidays are the key variables in increasing the accuracy of LSTM model, respectively. Our findings provide insights to facilitate the decision-making of managers and planners in public transportation planning sector to improve transit reliability and passengers' satisfaction level. [ABSTRACT FROM AUTHOR]

The digital economy is regarded as the new economy in the 21st century, and transportation infrastructure is the key driving force of traditional economic growth. However, there are still fewer studies on both. In this paper, 41 cities in the Yangtze River Delta (YRD) region are studied from 2011 to 2020. Regression using the generalized spatial least squares method confirms the facilitating effect of transportation infrastructure on the digital economy. For every 1% rise in the construction level, the digital economy improves by 0.016. Among them, enhancing the attractiveness of the core industries of the digital economy for innovation, entrepreneurial activities, and investment is an important facilitating mechanism. Further, we find that transportation infrastructure's facilitating role is enhanced after crossing the threshold of digitalization. In addition, upgrading industrial structure (rationalization and advancement) strengthens this role. The above findings reveal the relationship between transportation infrastructure and the digital economy in China and provide a scientific basis for emerging economies to seek transportation planning policymaking and digital economy development. [ABSTRACT FROM AUTHOR]

This study addresses forestry planning challenges arising from supply-demand imbalances. In forest planning, supply often exceeds demand because supplies are known in advance, while demands are known more short term when ordered. This leads to so-called "creaming," where forest planners select nearby areas first. With static supply and incremental demand information, average transportation distance increases over the planning horizon. To mitigate this, we propose an approach to artificially balance supply and demand. This can be achieved by including additional time periods with additional demand making up the factual difference. We evaluate three planning approaches to model the extended demand, varying the number of time periods and extension duration. Through simulations, we compare these approaches to traditional methods and theoretical solutions. Our proposed approach aims to better keep the average distance balanced throughout the overall planning periods. It ensures that average transportation distances are not excessively favorable in the initial periods, nor unreasonably high in the later periods, resulting in a favorable equilibrium in the average transportation distance over time. It makes sure that we do not need the additional truck capacity at certain times. We assess our proposed approaches using a case study from a Swedish forestry company, demonstrating their superiority over current practices. [ABSTRACT FROM AUTHOR]

The advent of autonomous vehicles (AVs) is poised to transform urban landscapes, notably by altering perceptions of travel time value, reshaping parking structures, and introducing additional travel demand through empty-cruising and self-parking capabilities. These changes are anticipated to influence both residential and parking location choices. This study delves into the complexities of these decisions by integrating insights from 28 modelling studies on residential and/or parking location choices in the context of AVs and by proposing an evaluation framework for future modelling efforts. A critical gap identified is the oversight of empty-cruising and parking costs in the context of residential relocation choice for owners of privately-owned AVs. Furthermore, no study has adopted a joint-choice modelling approach for residential and parking location choices. By offering guidelines for the incorporation of these elements, this study contributes to the development of a holistic residential and parking location choice model with AVs, thus providing a valuable roadmap for future research in urban planning and transportation economics. [ABSTRACT FROM AUTHOR]

This research presents a novel hybrid approach, which combines the White Shark Optimizer (WSO) metaheuristic algorithm with chaotic maps integrated into the binarization process. Inspired by the predatory behavior of white sharks, WSO has shown great potential to navigate complex search spaces for optimization tasks. On the other hand, chaotic maps are nonlinear dynamical systems that generate pseudo-random sequences, allowing for better solution diversification and avoiding local optima. By hybridizing WSO and chaotic maps through adaptive binarization rules, the complementary strengths of both approaches are leveraged to obtain high-quality solutions. We have solved the Set Covering Problem (SCP), a well-known NP-hard combinatorial optimization challenge with real-world applications in several domains, and experimental results indicate that LOG and TENT chaotic maps are better after statistical testing. This hybrid approach could have practical applications in telecommunication network optimization, transportation route planning, and resource-constrained allocation. [ABSTRACT FROM AUTHOR]

We quantified the impact of transit-oriented development (TOD) on consumer expenditures in seven sectors, including retail, healthcare, culture, and education, in Seoul, South Korea, employing propensity score matching, ordinary least square regression, and geographically weighted regression (GWR). Our findings revealed that TOD significantly increases consumer expenditures, with sectoral effects ranging between 20.1 and 21.3%. Crucially, the GWR analysis highlights the spatially dependent nature of TOD impact, uncovering substantial local variations. Districts such as Gangnam, Songpa, Gangdong, and Gangseo-gu exhibit pronounced positive effects, with consumer spending increases exceeding 88.3%, indicating the potential of TOD as a catalyst for economic growth in these strategic areas. Conversely, areas such as Dobong, Seodaemun, and Geumcheon-gu show marginally negative effects, suggesting that TOD benefits are not uniformly distributed and may pose challenges in certain contexts. This study contributes to the literature by providing empirical evidence of the economic impact of TOD across diverse sectors and offering valuable insights for transportation and urban planning, emphasizing the need for context-sensitive approaches to maximize TOD outcomes. [ABSTRACT FROM AUTHOR]

Traffic sensors are vital to the development and operation of Intelligent Transportation Systems, providing essential data for traffic monitoring, management, and transportation infrastructure planning. However, optimizing the placement of these sensors, particularly across large and complex statewide highway networks, remains a challenging task. In this research, we presented a novel search algorithm designed to address this challenge by leveraging information gradients from K-nearest neighbors within an embedding space. Our method enabled more informed and strategic sensor placement under budget and resource constraints, enhancing overall network coverage and data quality. Additionally, we incorporated spatial kriging analysis, harnessing spatial correlations of existing sensors to refine and reduce the search space. Our proposed approach was tested against the widely used Genetic Algorithm, demonstrating superior efficiency in terms of convergence time and producing more effective solutions with reduced information loss. [ABSTRACT FROM AUTHOR]

The impact of the Covid-19 pandemic on public transportation usage in Bursa, Turkey, was examined in this work. The usage of major transit systems (buses, trams and light rail) before and during the pandemic were assessed. Changes in public transportation behaviour were captured through an online survey of 767 participants from key activity centres. Using two ordinal logit models, the factors influencing preferences were identified. It was found that age, gender and home-to-work distance were influential before the pandemic, while educational status and distance became crucial during the pandemic. The research further explored the tendency to walk, cycle or use an e-scooter if safe, separated non-motorised transportation routes had been provided, considering various scenarios of home–workplace distance. The results of this study will help decision makers make sustainable transportation plans by taking into account catastrophic periods such as the Covid-19 pandemic. [ABSTRACT FROM AUTHOR]

This paper introduces a deterministic algorithm to solve the discrete network design problem (DNDP) efficiently. This non‐convex bilevel optimization problem is well‐known as an non deterministic polynomial (NP)‐hard problem in strategic transportation planning. The proposed algorithm optimizes budget allocation for large‐scale network improvements deterministically and with computational efficiency. It integrates disjunctive programming with an improved partial linearized subgradient method to enhance performance without significantly affecting solution quality. We evaluated our algorithm on the mid‐scale Sioux Falls and large‐scale Chicago networks. We assess the proposed algorithm's accuracy by examining the objective function's value, specifically the total travel time within the network. When tested on the mid‐scale Sioux Falls network, the algorithm achieved an average 46% improvement in computational efficiency, compared to the best‐performing method discussed in this paper, albeit with a 4.17% higher total travel time than the most accurate one, as the value of the objective function. In the application to the large‐scale Chicago network, the efficiency improved by an average of 99.48% while the total travel time experienced a 4.34% increase. These findings indicate that the deterministic algorithm proposed in this research improves the computational speed while presenting a limited trade‐off with solution precision. This deterministic approach offers a structured, predictable, and repeatable method for solving DNDP, which can advance transportation planning, particularly for large‐scale network applications where computational efficiency is paramount. [ABSTRACT FROM AUTHOR]

In the context of rapid urbanization, accurately identifying the visual factors that influence environmental safety perception is crucial for improving urban transportation environments and enhancing pedestrian safety. With the increase in urban population density and traffic flow, optimizing urban environmental design to elevate residents' sense of safety has become a key issue in urban planning and management. However, the existing studies face numerous challenges in conducting large-scale quantitative analysis of environmental safety perception in complex scenarios, such as difficulties in data acquisition and limitations in analytical methods. This study addresses these challenges by applying image semantic segmentation and object detection techniques to extract key visual elements from street view images, combined with manual scoring and deep learning methods, to construct a road safety perception dataset. Using a LightGBM model and the SHAP interpretation framework, in this study, we identify the critical visual factors influencing environmental safety perception. An empirical study was conducted in Macau, a modern city where Eastern and Western cultures intersect, and tourism thrives. The findings reveal that: ① The overall environmental safety perception in the eight parishes and surrounding roads of Macau is relatively high, with significant regional differences in safety perception scores around Macau's parish roads; ② The proportions of buildings, sidewalks, roads, and trees in images are the four primary factors influencing environmental safety perception; ③ The proportions and quantities of visual elements interact with each other, and their reasonable distribution helps form clear spatial visibility and creates conducive activity spaces, thereby enhancing the perception of environmental safety. Through empirical analysis, this study uncovers the mechanisms by which visual elements in urban street scenes affect environmental safety perception, providing scientific evidence for urban planning and transportation environment improvement. The research holds theoretical significance and offers practical references for urban design and management, demonstrating broad application value. [ABSTRACT FROM AUTHOR]

Space is often produced digitally before it is produced physically. This article investigates how the right to the city can be broadened to include the appropriation of digital spaces to produce 'lived' transportation spaces. Focussing on mobilisation against highway expansion in Dallas, Texas, we ask the following: (1) what are the mechanisms through which space is conceived, perceived, and lived through the lens of mobility justice; (2) how might claims for technical information challenge dominant transportation policies and projects; and (3) how might participants inhabit digital spaces? We conduct a qualitative analysis of transportation planning narratives, visualisations, and public comments in three documents: the Dallas City Center Master Assessment Process, Coalition for a New Dallas' I-345/45 Framework Plan, and public survey data regarding proposed highway changes (n = 1241). Findings demonstrate how residents challenge transportation 'needs' as often determined in conceptual planning spaces. Further, technologies can be appropriated to produce differential spaces, which can alter the trajectory of highway projects. Challenging the legitimacy of institutionalised knowledge through the appropriation and production of digital spaces forms part of a larger claim to the right of the city. [ABSTRACT FROM AUTHOR]

Annual average daily traffic (AADT) is one of the most inevitable elements for both transportation planning and traffic safety analysis. For state Departments of Transportations (DOTs), collecting AADT data is a critical and demanding task, normally accomplished through a combination of permanent and temporary traffic count stations, which has been proved to be extremely labor-intensive and time-consuming. Consequently, due to limited resources, it is typically performed for the state-maintained highways rather than the low-volume roadways maintained by town jurisdictions. Therefore, it is necessary to develop innovative and cost-effective approaches to generate AADT for low-volume roadways while still maintaining accuracy, including data driven analytical methodologies. To this end, this study conducted a comprehensive literature review of methodologies and relevant data used for AADT generation and estimation. Based on the data availability, data elements used for modeling AADT in Connecticut (CT) were collected. Specifically, AADT data for town-maintained highways from 2016 to 2020 was collected from the Streetlight platform and calibrated to fit the local condition in CT. Furthermore, machine learning algorithms were developed to predict future AADT beyond 2020. The model validation results indicate that the AADT estimated by this study is robust and reliable in terms of prediction accuracy, and it can serve as a valid asset in transportation planning and traffic safety analysis to practitioners and transportation agencies. [ABSTRACT FROM AUTHOR]

Accurate traffic prediction is crucial for optimizing taxi demand, managing traffic flow, and planning public transportation routes. Traditional models often fail to capture complex spatial–temporal dependencies. To tackle this, we introduce the Spatio-Temporal Embedding Fusion Transformer (STEFT). This deep learning model leverages attention mechanisms and feature fusion to effectively model dynamic dependencies in traffic data. STEFT includes an Embedding Fusion Network that integrates spatial, temporal, and flow embeddings, preserving original flow information. The Flow Block uses an enhanced Transformer encoder to capture periodic dependencies within neighboring regions, while the Prediction Block forecasts inflow and outflow dynamics using a fully connected network. Experiments on NYC (New York City) Taxi and NYC Bike datasets show STEFT's superior performance over baseline methods in RMSE and MAPE metrics, highlighting the effectiveness of the concatenation-based feature fusion approach. Ablation studies confirm the contribution of each component, underscoring STEFT's potential for real-world traffic prediction and other spatial–temporal challenges. [ABSTRACT FROM AUTHOR]

Estimating origin-destination (OD) demand is integral to urban, regional, and national freight transportation planning and modeling systems. However, in developing countries, existing studies reveal significant inconsistencies between OD estimates for domestic and import/export commodities derived from interregional input-output (IO) tables and those from regional IO tables. These discrepancies create a significant challenge for properly forecasting the freight demand of regional/interregional multimodal transportation networks. To this end, this study proposes a novel integrated framework for estimating regional and international (import/export) OD freight flows for a set of key commodities that dominate long-distance transportation. The framework leverages multisource data and follows a three-step process. First, a spatial economic model, PECAS activity allocation, is developed to estimate freight OD demand within a specific region. Second, the international (import and export) freight OD is estimated from different zones to foreign countries, including major import and export nodes such as international seaports, using a gravity model with the zone-pair friction obtained from a multimodal transportation model. Third, the OD matrices are converted from monetary value to tonnage and assigned to the multimodal transportation super network using the incremental freight assignment method. The model is calibrated using traffic counts of the highways, railways, and port throughput data. The proposed framework is tested through a case study of the Province of Jiangxi, which is crucial for forecasting freight demand before the planning, design, and operation of the Ganyue Canal. The predictive analytics of the proposed framework demonstrated high validity, where the goodness-of-fit (R2) between the observed and estimated freight flows on specific links for each of the three transport modes was higher than 0.9. This indirectly confirms the efficacy of the model in predicting freight OD demands. The proposed framework is adaptable to other regions and aids practitioners in providing a comprehensive tool for informed decision-making in freight demand modeling. [ABSTRACT FROM AUTHOR]

There is a complex interaction between national land space and transportation. China is accelerating its strategy of building a strong transportation country and territorial spatial planning, which requires evaluating and strengthening the coordination relationship between transport and land space to promote development. Taking the Beijing–Tianjin–Hebei urban agglomeration in China as an example, this study analyze the high-speed transportation construction and land space development process, evaluates the coupling and coordination degree between high-speed transportation and land space, as well as the "development-protection" situation in the years 2000, 2010, and 2020 by means of the coupling and coordination model, spatial autocorrelation analysis, and GIS analysis. The result shows: (1) high-speed transportation construction and territorial space development have been active for a long time, and most of the cities have maintained a high level of increase in these two indicators. In terms of protection indicators, Tangshan and other coastal cities have been lagging behind for a long time without any improvement trend. (2) The coupling and coordination level between high-speed transportation and development is high, with slight weakening over time, with an average annual decrease of less than 0.09%. The change in the coupling coordination level with protection shows the wave dynamics of 'low-middle-low'. (3) In the future, high-speed transportation will have a significant impact on land space in the country for a long time. In this regard, it is suggested to: (1) promote the construction of high-speed transportation for network, systematic, and serial development; (2) accelerate the extension of the high-speed transportation advantages of the existing growth poles of Beijing and Tianjin; and (3) develop Shijiazhuang and Langfang as the new growth poles. This study will promote the integration of transportation and urban planning, which will contribute to the development of high-quality synergistic urban agglomerations around the world. [ABSTRACT FROM AUTHOR]

To implement the requirements of "Building a Strong Transportation Nation" and the "Dual-Carbon Goals", and guided by the "Action Plan for Green Urban Rail Transit Development in Chinese Cities", this study explores green and low-carbon analysis and evaluation methods in the rail network planning stage from three aspects: urban space, integrated transportation, and rail transit. A comprehensive theoretical framework was systematically constructed, covering macro to micro levels. The analysis process employed various methods, including model building, spatial econometrics, geographic analysis, and passenger flow prediction, to establish a theoretical structure for the green and low-carbon effects of rail transit through both quantitative and qualitative approaches. The study proposed a tripartite green development framework integrating "City-Transportation-Railway", forming a primarily quantitative evaluation index system that effectively promotes green travel. This system aligned with higher-level planning while considering the unique characteristics of rail transit, guiding the next phase of green urban rail implementation and related research. The research results demonstrated that combining elements of rail network planning with urban and integrated transportation could effectively and quantitatively reflect green development goals. The calculation methods for evaluation indicators were feasible, operational, and comparable. This framework not only meets the requirements and goals of national land spatial planning and integrated transportation planning but also provides valuable guidance for subsequent stages of construction planning, feasibility studies, and preliminary designs in green development. Furthermore, it aligns well with the entire planning and design process of rail transit. The conclusions and recommendations from this study can be effectively incorporated into national land spatial planning and integrated transportation planning to ensure a unified planning blueprint. [ABSTRACT FROM AUTHOR]

As the value and importance of urban air mobility (UAM) are being recognized, there is growing attention towards UAM. To ensure that urban air traffic can serve passengers to the greatest extent while ensuring safety and generating revenue, there is an urgent need for a transportation scheduling plan based on safety considerations. The region of Beijing–Tianjin–Hebei was selected as the case study in this research. A real-time demand transportation scheduling model for a single day was constructed, with the total service population and total cost as objective functions, and safety intervals, eVTOL performance, and passenger maximum waiting time as constraints. A Joint Optimization of Cost and Scheduling Particle Swarm Optimization (JOCS-PSO) algorithm was utilized to obtain the optimal solution. The optimal solution obtained in this study can serve 138,610,575 passengers during eVTOLs' entire lifecycle (15 years) with a total cost of CNY 368.57 hundred million, with the cost of CNY 265.9 per passenger. Although it is higher than the driving cost, it saves 1–1.5 h and thus has high cost effectiveness during rush hours. [ABSTRACT FROM AUTHOR]

Traffic prediction is essential for transportation planning, resource allocation, congestion management and enhancing travel experiences. This study optimizes data preprocessing techniques to improve machine learning-based traffic prediction models. Data preprocessing is critical in preparing the data for machine learning models. This study proposes an approach that optimizes data preprocessing techniques, focusing on flow-based analysis and optimization, to enhance traffic prediction models. The proposed approach explores fixed and variable orders of data preprocessing using a genetic algorithm across five diverse datasets. Evaluation metrics such as root mean squared error (RMSE), mean absolute error (MAE) and R-squared error assess model performance. The results indicate that the genetic algorithm’s variable order achieves the best performance for the ArcGIS Hub and Frementon Bridge Cycle datasets, fixed order one preprocessing for the Traffic Prediction dataset and variable order using the genetic algorithm for the PeMS08 dataset. Fixed order 2 preprocessing yields the best performance for the XI AN Traffic dataset. These findings highlight the importance of selecting the appropriate data preprocessing flow order for each dataset, improving traffic prediction accuracy and reliability. The proposed approach advances traffic prediction methodologies, enabling more precise and reliable traffic forecasts for transportation planning and management applications. [ABSTRACT FROM AUTHOR]

The deployment of public electric vehicle charging stations (EVCS) is a critical component of transportation electrification. Recent studies have highlighted growing concerns about disparities in accessibility to public chargers between different demographic groups. This research expands ongoing equity concerns by contextualizing existing transportation equity discourse and analyzing public charger access disparities in Austin, Texas. Using threshold equity toolkits, we investigated public EVCS access disparity across different races and income groups. We conducted a generalized additive model regression to measure and visualize the effects of possible determinants on public EVCS access. The analysis results revealed that a public EVCS access disparity exists in Austin, with most chargers being installed in areas where the majority of the population is Non-Hispanic White. There was a more equal distribution of public EVCSs across income quartiles when compared with race. However, middle- and high-income groups had better access than lower-income communities in terms of distance to the nearest public EVCSs. Our regression analysis found that regional and socio-demographic factors, such as race and income, have a statistically significant impact on public charger access. The regression analysis also revealed that Austin's current public EVCS deployment seems to favor communities above the poverty level and with higher numbers of registered electric vehicles. Local policymakers should reflect on the findings of this study to develop an equitable transportation electrification plan. Federal environmental justice plans such as the Justice40 initiative can benefit from incorporating more local contexts to better invest in disadvantaged communities. [ABSTRACT FROM AUTHOR]

Urban Transportation Planning is increasingly being brought into focus by the interaction between land use and transportation services. Urban transportation includes all kinds of products, from pedestrian vehicles and movements to land use. Especially in rapidly developing areas, the distance between land use and transportation needs to be more careful and planned. As urbanization grows, transportation problems gradually increase, and this situation needs to be resolved. Where urbanization expands, the concept of accessibility emerges. Hiding in an accessibility place. In our study, the interaction between urban transportation planning and land use in Ankara was examined. Structures have been made on the effects of Ankara population data on transportation. In particular, the improvement in the openness of rail systems and EGO buses was discussed. With the proliferation of bicycles, how bicycle transportation was included in land use and what kind of practices were implemented were discussed, and the bicycle paths planned in Ankara were examined. Investigations were made on how the increasing population and transportation were affected by the development of shopping centers and industrial zones, and a transportation map of the industrial zones was prepared. As a result, continuity should be ensured in the planning decisions developed for Ankara province. Continuous Master Development Plan should be implemented. [ABSTRACT FROM AUTHOR]

A fine-grained metro trip contains complete information on user mobility, including the original station, destination station, departure time, arrival time, transfer station(s), and corresponding transfer time during the metro journey. Understanding such detailed trip information within a city is crucial for various smart city applications, such as effective urban planning and public transportation system optimization. In this work, we study the problem of detecting fine-grained metro trips from cellular trajectory data. Existing trip-detection approaches designed for GPS trajectories are often not applicable to cellular data due to the issues of location noise and irregular data sampling in cellular data. Moreover, most cellular data-based methods focus on identifying coarse-grained transportation modes, failing to detect fine-grained metro trips accurately. To address the limitations of existing works, we propose a novel and efficient fine-grained metro-trip detection (FGMTD) model in this work. By considering both the local and global spatial–temporal characteristics of a trajectory and the metro network, FGMTD can effectively mitigate the effects of location noise and irregular data sampling, ultimately improving the accuracy and reliability of the detection process. In particular, FGMTD employs a spatial–temporal hidden Markov model with efficient index strategies to capture local spatial–temporal characteristics from individual positions and metro stations, and a weighted trip-route similarity measure to consider global spatial–temporal characteristics from the entire trajectory and metro route. We conduct extensive experiments on two real datasets to evaluate the effectiveness and efficiency of our proposed approaches. The first dataset contains cellular data from 30 volunteers, including their actual trip details, while the second dataset consists of data from 4 million users. The experiments illustrate the significant accuracy of our approach (with a precision of 87.80 % and a recall of 84.28 % ). Moreover, we demonstrate that FGMTD is efficient in detecting fine-grained trips from a large amount of cellular data, achieving this task within 90 min of processing a day's data from 4 million users. [ABSTRACT FROM AUTHOR]

A work zone is an area of roadway with construction, maintenance, or utility work activities. A work zone is typically marked by signs, channelizing devices, barriers, pavement markings, and/or work vehicles. It extends from the first warning sign or rotating/strobe lights on a vehicle to the end road work sign or the last temporary traffic control device. As road reconstructions are an opportunity to eliminate or mitigate the risk of unsafe situations with relatively small and short-term investments, it is necessary to enable the preparation of road works in the most efficient way possible, with maximum safety for all road users in road work zones, both drivers and workers. For users who are not familiar with the network, the work zone can be a potential place for accidents. The marking of the work zone on the road is carried out with the help of vertical, horizontal and light traffic signals, and its provision is carried out with appropriate equipment. Managing traffic during construction is necessary to minimize traffic delays, maintain motorist and worker safety, complete roadwork in a timely manner, and maintain access for businesses and residents. Effective work zone traffic management includes assessing work zone impacts and documenting strategies for mitigating the impacts in a transportation management plan. Work zone traffic management strategies should be identified based on the project constraints, construction phasing/staging plan, type of work zone, and anticipated work zone impacts. Once these strategies are implemented, they need to be monitored to ensure they effectively manage work zone impacts. The aim of this paper is to define the traffic regime, i.e. the traffic signaling and equipment during the rehabilitation and repair of state road R1301, section Ohrid - Sveti Naum, subdivision Konjsko - BC Sveti Naum. [ABSTRACT FROM AUTHOR]

This paper introduces link information value obtained by the traffic sensors and presents a traffic sensor location and flow estimation joint optimization model in an urban road network. In contrast to most previous studies, this paper adds new traffic sensors into the existing sensor network and proposes a data-driven path flow measurement method based on Wasserstein metric, which is utilized to measure the distance between the estimated traffic flow distribution and the actual distribution. Furthermore, this paper develops a customized greedy algorithm by combining a search strategy for the link information value to obtain the optimal sensor location scheme and perform traffic flow estimation under different budget conditions. Numerical experiments are conducted on Sioux-Falls test network and Eastern Massachusetts interstate highway subnetwork to verify the accuracy and effectiveness of the proposed model based on Wasserstein metric and the developed solution method. Computational results show that the sensor location scheme generated by the model based on Wasserstein metric can reduce the estimation error of the traffic flow compared with KL divergence model under the same deployment cost. Additionally, the customized greedy algorithm can achieve the better performance than the Brute force algorithm in terms of computing time and solution quality. • A traffic sensor location and path flow estimation joint optimization model is formulated. • A data-driven path flow measurement method based on Wasserstein metric is proposed. • A customized greedy algorithm by combining a search strategy is developed. • Numerical experiments are conducted based on the real-word transportation network. [ABSTRACT FROM AUTHOR]

Prediction using computer vision is getting prevalent nowadays because of satisfying results. The vision of Internet of Vehicles (IoV) expedites Vehicle to everything (V2X) communications by implementing heterogeneous global networks. Road crack is one of the major factors that causes road mishaps and damage to vehicles. To ensure smooth and safe driving, avoiding road crack in transportation planning and navigation is significant. To address this issue, we proposed a novel convolutional neural network (CNN)-based smart transportation system. We showed how to quantify the severity of the cracks. We proposed a post-processing algorithm to provide option to the driver to select the safest road toward the destination. The communication system for the proposed smart transportation system has also been introduced. The performance comparison of a few popular CNN architectures has been investigated. Simulation results showed that Resnet50 algorithm provides significantly high accuracy compared with SqueezeNet and InceptionV3 algorithm in order to detect road cracks for the proposed transportation system. We demonstrated high accuracy of measuring the crack severity via numerical analysis. The integration of the proposed system in next generation smart vehicles can ensure accurate detection of road cracks earlier enough providing the option to select alternate safe route toward a destination as advanced driver assistance service. Moreover, the proposed system can also play a key role in order to reduce road mishaps notably by warning the driver about the updated road surface conditions. [ABSTRACT FROM AUTHOR]

Local authorities have a strategic role in mitigating the environmental impacts of the transport sector. However, they struggle to integrate environmental issues into their decision-making processes, especially planning. In the European context of the Sustainable Urban Mobility Plan approach and Strategic Environmental Assessments (SEAs), this paper scrutinises three French localities to determine the current best practices and limitations for designing mobility plans and integrating environmental issues. Several limitations are identified: (1) limited expertise in defining and characterising actions and objectives, which complexifies plans' design, understanding, and monitoring; (2) a lack of a framework to conduct long-term quantitative environmental assessments and to use the results to influence decision effectively; and (3) monitoring processes are barely described in the documents, and the planning horizon where objectives are defined is not in sync with the indicators' mandatory evaluation period. This French case study thus reveals that European planning practices must be further analysed and improved to deal with the rising environmental concerns, e.g. through an operational framework to design mobility plans with effective integration of environmental issues. • Planning supports local authorities in moving towards a more sustainable mobility. • There is no robust approach to defining and characterising actions and objectives. • Robust long-term quantitative assessments are lacking to support planning. • Monitoring modalities are scarcely described in plans and SEAs. • Improving planning frameworks would help the integration of environmental issues. [ABSTRACT FROM AUTHOR]

Sustainable transport policies are fundamental to adapt transport capacity to existing and future travel demand. In this context, this study aims to develop a System Dynamics (SD), to verify the effects of these policies, focusing on congestion and air pollution. Combined with the SD, the discrete choice utility approach was used to predict the modal share in different policy spaces. In addition, it is carried out a case study in Rio de Janeiro in two realities: pre- and post-pandemic. The results show how mitigation policies can reduce transport externalities (congestion and pollution). The encouragement of high-capacity public transport and car ownership control are the best measures, obtaining high simulation scores. The post-pandemic scenario shows that reducing travel demand is the key to achieving better results. All scores obtained in this scenario are better than in pre-pandemic scenario. Finally, results point out that ride-hailing should be used in a conscious way. [ABSTRACT FROM AUTHOR]

This study develops an integrated methodology to incorporate public perspectives into the establishment and development of public transportation infrastructure systems. The approach involves surveying citizens to collect data, performing demographic analyses to identify differences between cities, and applying Multi-Criteria Decision-Making (MCDM) techniques to weight, scale, and integrate evaluation criteria in order to determine the optimal transportation option. The primary aim of this research is to incorporate public perspectives into transportation planning in developing countries and to promote stakeholder engagement for transportation initiatives in cities such as Baghdad, Iraq, and Tehran, Iran. First, an initial survey was conducted to identify the top three preferred criteria among 200 participants from both cities. The survey results revealed that the three most important criteria were safety, travel time, and reliability. Subsequently, a larger survey utilizing the Saaty scale was administered to capture citizens' preferences, with a total sample size of 550 from Baghdad and 345 from Tehran. The weights of the criteria were then calculated using the Group Analytical Hierarchy Process (GAHP). Three transportation alternatives—monorail, Light Rapid Transit (LRT), and metrobus—were suggested by transportation experts to be evaluated and ranked using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based on the weighted citizen preferences. The results indicate that for Baghdad residents, transportation safety is the most important priority, followed by reliability and travel time. However, LRT is rated as the most optimal transportation solution (0.721), followed by monorail (0.596) and metrobus (0.078). In Tehran, travel time represents the most preferred transportation attribute, followed by reliability and safety. The residents of Tehran are shown to prefer LRT (0.843), followed by monorail (0.370) and metrobus (0.143). Despite the similar ranking of transportation alternatives in the two cities, the performance scores differ between them, highlighting the importance of tailoring transportation planning to the unique preferences and needs of local communities. The validation of the results was conducted through sensitivity analysis to determine how variations in the criteria weights and input parameters affected the final rankings. Additionally, a stated preference survey was employed as a practical method to evaluate the robustness of the final ranking of the alternatives. [ABSTRACT FROM AUTHOR]

The current trajectory planning methods for multi‐robot systems face challenges due to high computational burden and inadequate adaptability in complex constrained environments, obstructing efficiency improvements in production and logistics. This article presents an innovative solution by integrating model predictive contouring control (MPCC) and long short‐term memory (LSTM) networks for real‐time trajectory planning of multiple mobile robots. Based on the datasets generated by MPCC, a customized LSTM network is constructed to learn the collaborative planning behavior from these datasets offline, subsequently producing smooth and efficient trajectories online with a low computational burden. Moreover, a hybrid control scheme, incorporating a lidar‐based safety evaluator, avoids unexpected collision risks by switching to MPCC when necessary, ensuring the overall safety and reliability of the multi‐robot system. The proposed hybrid LSTM method is implemented and tested in the robot operating system (ROS) within diverse constrained scenarios. Experimental results demonstrate that the hybrid LSTM method achieves ≈6% enhancements in trajectory productivity and a reduced computational burden of roughly 75% compared to MPCC, thereby providing a promising solution for local multi‐robot trajectory planning in logistics transportation tasks. [ABSTRACT FROM AUTHOR]

The further reform and development of engineering education courses can help improve the teaching quality of higher education institutions, implement the fundamental task of talent cultivation, and provide important support and channels for cultivating high‐quality and supportive engineering technical talents. The traffic system simulation course is an applied science closely related to engineering technology. It mainly cultivates senior engineering technical talents engaged in research and development of transportation engineering, analysis of road traffic characteristics, and transportation planning and design. Considering the characteristics of the traffic system simulation course, this study puts forward the course teaching design based on computer virtual reality software. Combined with the training objectives of applied talents in the traffic system simulation course, this study focuses on how to reform the teaching content and teaching mode with the help of Vissim computer simulation software. The course teaching design refines and optimizes the teaching content by the use of computer software. The use of Vissim software in the course enriches the teaching content and methods and stimulates students' interest in learning. Through the guidance of active exploration and self‐directed learning for students, the traffic system simulation course reform has created a more positive and effective learning environment for students, which helps to cultivate their exploratory spirit and innovative ability. The implementation of the course teaching reform has gradually improved students' learning initiative and ability to master course content, as well as their practical problem‐solving abilities. Good teaching results have been achieved. [ABSTRACT FROM AUTHOR]

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The COVID-19 pandemic has generated renewed interest in how telecommuting can alter the workings of our cities and regions, but there is little guidance on how to align planning practice with the new reality. This report synthesizes the research on telecommuting and its consequences to help planners better understand what effects may occur from the proliferation of telecommuting and what lessons can be drawn from research findings. Emphasis is on the broad relevance of telecommuting to many domains of planning, including housing, land use, community development, and inclusive place-making, while attention is paid to changes in travel demand, vehicle miles traveled (VMT), and greenhouse gas emissions. The research suggests that telecommuting can occur in a variety of ways, and its impacts are largely dependent not only on the type/schedule of telecommuting but on the built environment, transit accessibility, and other amenities/opportunities the location provides. The varying impacts reported in the research can be seen as an encouragement for planners to actively create a better future rather than merely responding to the rise of telecommuting. Given the breadth of telecommuting’s impacts, systematic coordination across various planning domains will be increasingly important. This report also calls for collaboration across cities to guide the ongoing transformation induced by telecommuting not in a way that leads to more residential segregation but in a way that provides more sustainable and inclusive communities.