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The Journal of Geographical Systems (JGS) has announced two initiatives to recognize outstanding research published in the journal. The first initiative is the JGS Best Paper Award, which aims to encourage and acknowledge excellent scholarship in the field of GIScience and spatial planning. The second initiative is the Editors' Choice, where the editors select their favorite paper from each issue. The winners of these awards are listed on the journal's website and their papers are made freely accessible for a certain period of time. The 2023 JGS Best Paper Award was given to Luyu Liu, Adam Porr, and Harvey J. Miller for their research on evaluating the reliability of public transit accessibility using real-time data. The paper demonstrates how even small levels of unreliability in transit services can significantly impact accessibility levels, particularly for lower income populations. The paper also promotes open and reproducible research practices. The Editors' Choice for Volume 26(1) is a paper by Chris Jacobs-Crisioni, Ana I. Moreno-Monroy, Mert Kompil, and Lewis Dijkstra, which proposes a sequential approach to estimate school provision, cost, and access using open databases of student counts. The paper provides valuable insights into the balance between cost, access, and residential values in school provision. Both papers are recognized for their significant contributions to the field. [Extracted from the article]

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]

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]

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]

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]

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]

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]

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]

Under the background of green and low‐carbon transformation of industrial high‐energy‐consuming parks, in order to realize the coordinated development planning of energy and transportation under different peaking capacity requirements, this paper proposes a collaborative optimization model of industrial high‐energy‐consuming parks under green and low‐carbon. Firstly, by studying the energy balance mechanism between industrial load and new energy, an energy topology prior model of energy interaction in the next generation of industrial new energy power grid is constructed. Then, based on the probability characteristics of the equilibrium state of different regions in the high‐energy‐consuming industrial zone, combined with the prior knowledge of industrial load and low‐carbon power system, a collaborative development planning model of high‐energy‐consuming industrial parks is constructed. Finally, the proposed model is simulated. From the simulation results the authors can see the dynamic division model and solution algorithm of energy balance region established in this paper can effectively improve the energy balance ability of power grid, reduce peak shaving demand and use more renewable energy. [ABSTRACT FROM AUTHOR]

To enhance the accessibility of transportation systems in urban agglomerations and mitigate carbon emissions from traffic, this paper introduces a comprehensive planning approach for transportation routes. Initially, it calculates the carbon emissions generated by urban mass transit, and set minimizing these emissions as a key objective. Subsequently, for road traffic within urban agglomerations, a layout model is established that aims to minimize both travel time and carbon emissions. By incorporating the Pareto optimal concept optimization algorithm, the study determines the optimal solution. In terms of rail transit, the paper establishes the optimal planning outcome by employing the probability density function of rail transit as a constraint, after determining the appropriate scale of the system. Experimental results demonstrate that this approach effectively controls the overall carbon emissions of the urban agglomeration transportation system, with the layout coordination coefficient peaking at 0.926. [ABSTRACT FROM AUTHOR]

Fine grid management is one of the important development directions of transportation planning that has not been fully considered in previous literature. This paper explores the application of the fine grid management method in transportation planning. Based on a case study of Chuanhui, China, this paper proposes a data-driven fine grid partitioning method for determining traffic analysis zones (TAZs). The TAZs are partitioned based on quadrilateral and hexagonal grids. This paper also summarizes a set of criteria for evaluating the impact of different fine grid partitioning methods based on the geographically and temporally weighted regression (GTWR) model. The results show that our fine grid partitioning method for determining TAZs based on quadrilateral grids can achieve a relatively low level of predicted value bias and variable correlation degree bias when the number of TAZs is larger, and it has obvious advantages. Finally, policy implications are proposed to promote the refinement of transportation planning. [ABSTRACT FROM AUTHOR]

Tourist-dedicated train is a tourism product launched by railway transportation enterprises that combines transportation with tourism. The preparation of tourist-dedicated train line plan is an important part of tourist-dedicated train operation. The optimization of the tourism train line plan is based on its characteristics that are different from other passenger trains. Based on the analysis of the demand for the line plan of the tourist-dedicated train, this paper constructs the optimization model of the line plan with the goal of maximizing the income of the line plan and the constraints of transportation capacity, passenger flow and supply factors, and designs the corresponding genetic algorithm. The model is verified by an example. The verification results show that the optimal selection model and algorithm can better reflect the characteristics of the tourist-dedicated train line plan. The optimization results show that the reasonable matching of transportation supply and demand can provide decision support for the optimization of the tourist-dedicated train line plan. [ABSTRACT FROM AUTHOR]

Scholarly discourse and global policies advocate policy integration across sectors, including land use and transportation. However, African cities lack best practices for transport and land use integration. This paper employed a 'plan quality evaluation framework' and key informant interviews to assess the quality of decentralized plans of the Kumasi Metropolis regarding integrating transportation planning considerations. The integration of transport considerations into the goals and policies of the KMA's plans was weak. Although coordination and implementation arrangements were strong, provisions for monitoring and evaluation were inadequate. Institutional fragmentation, poor inter-departmental coordination, and resource constraints were integration hurdles that needed immediate attention. [ABSTRACT FROM AUTHOR]

To address the growing uncertainties in the future transportation industry, planners and engineers have identified and implemented scenario planning, a multi-step and strategic approach, in long-range transportation planning. A comprehensive examination of how equity is envisioned, planned, and analyzed during this process remains absent, despite several key guidelines of scenario planning exercises. This paper conducts a systematic literature review focusing on how equity has been considered and evaluated in scenario planning. The resources include academic papers, agency reports, and materials from the recent scenario planning conference, the 2nd Conference on Scenario Planning in Transportation hosted by the Transportation Research Board. This paper examines equity in scenario planning in three important ways: 1) the ways equity has been integrated across steps; 2) how equity analysis has evolved; and 3) when it is necessary to consider equity. This work adds value to the existing body of knowledge by presenting the policy implications based on the review of the current scenario planning research and practices. Early policy recommendations include the need for more vertical equity evaluations, the construction of robustness and justice indicators, and the adoption of a flexible mindset of planning for rather than being fixated on a single scenario. • A systematic literature review focusing on equity analysis in scenario planning. • It is vital to conduct policy robustness analyses incorporating equity. • More vertical equity research needs to be incorporated. • Having the mindset of equity planning for in more than one scenario is critical. [ABSTRACT FROM AUTHOR]

Quality control (QC) is an essential component of quality assurance (QA) programs used to monitor and ensure the highway construction projects' quality. After receiving the contractor's QC plan, state departments of transportation (DOTs) conduct review and acceptance, monitor the QC process, and ensure construction quality by performing inspection activities. Given that state DOTs are increasingly implementing alternative contracting methods (ACMs), including construction manager/general contractor (CM/GC), design-build (D-B), and public–private partnerships (P3), it is essential to consider the impact of shifting more responsibility to the contractor on quality management activities, including developing and monitoring QC plans throughout the project development. The objective of this paper was to investigate the implementation of QC plans for administering QA specifications in highway infrastructure projects delivered using ACMs. The data collected from a national survey questionnaire of 43 DOTs in the US and structured interviews with seven DOT representatives were employed to investigate this research's objective. As a result, eight typical requirements of QC plans in ACM projects were identified in terms of certification and qualification of QC personnel and process control. Additionally, the standard of care of QC staff in ACMs was not found to be higher than that in design-bid-build. Nine risk factors related to the oversight of QC plans in D-B, CM/GC, and P3 highway projects were identified. There was no statistical difference in the nine risk scores among D-B, CM/GC, and P3 projects. The findings from seven case studies provide insights regarding the implementation of QC plans for ACM highway projects. This study contributes to the body of knowledge by advancing the understanding of how state agencies execute and oversee QC activities in ACM projects. The outcomes of this study may help state DOTs identify optimal QC practices to administer the QA process in ACM projects. This study investigates the current state of practice in implementing QC plans to manage and improve QA processes for highway projects delivered using ACMs such as CM/GC, D-B, and P3. This study synthesized and documented typical QC requirements used by state DOTs in reviewing, accepting, and monitoring contractor compliance with their QC plans. Additionally, the descriptive and inferential testing results of nine risk factors in implementing QC plans in highway projects found in this study enable highway agencies to effectively justify their monitoring process of the QC plan implementation. Further, the results from seven case studies provide insights and key lessons learned regarding (1) the guidance and templates for developing a QC plan; and (2) risk perceptions in managing the QC plan implementation in ACM highway projects that can benefit both the contractors and state DOTs. The outcomes of this study also shed light on exploring the optimal allocation of responsibilities between state DOTs and contractors in implementing QC activities to administer QA programs in ACM highway projects. [ABSTRACT FROM AUTHOR]

To ensure the safety of the hogs in large-scale pig farming, the hog transfer truck needs to be cleaned and disinfected at high temperatures. This paper proposes a model of the transfer truck drying room based on spherical air outlets, using RC-4HC temperature and humidity measuring instruments, electronic scale, and other devices to measure the drying process of the transfer trucks at different outdoor ambient temperatures, different deck placement techniques, and different degrees of preheating, and explores the problem of excessive energy consumption in the transfer truck drying room. The outcome demonstrates that the transfer truck drying chamber has the fastest drying time—3137 s. The drying room's energy use is also significantly influenced by the transfer truck leaching effect, deck positioning, which has the second-highest impact, and the outdoor ambient temperature, which has a negligible effect. The fuel consumption of the drying room is decreased by 5.19% and 25.78%, respectively, when the second truck is preheated for 600 s and 1500 s utilizing the leftover heat from the drying of the first truck. The resultant energy savings are astounding. [ABSTRACT FROM AUTHOR]

Development of a reliable and intelligent traffic monitoring system is highly desired to improve the transportation safety and establish future transportation plans due to the fast growth of vehicle population. Vehicle classification is one of the most critical subsystems where existing ones suffer from privacy concerns, requirements of complicated systems, and high maintenance cost. This paper reports a novel vehicle classification method by utilizing a triboelectric sensor to accurately identify vehicles. Novelty of this method originates from using triboelectric sensor and machine learning method with important advantages over current alternatives by providing an easy installation, simple operation, noninvasive measurement, cost-effective manufacturing, and highly accurate classification. To make a classification, vehicle toys' signals were acquired from triboelectric sensor and then applied to a deep learning algorithm. The 1932 sensor output data were grouped into a set of seven vehicle toys with different wheelbases, and number of tires passing on are used to train and optimize 1D-CNN model. The utilized 1D-CNN model achieved accuracy, f1-score, precision, and recall as 96.38%, 0.9638, 0.9658, and 0.9637, respectively. [ABSTRACT FROM AUTHOR]

With the rapid development of global industrialization, the consumption of refined oil is at a high level. The scale of refined oil transportation is huge. For refined oil sales enterprises, determining a method for making reasonable transportation plans and reducing transportation cost has become an important means to improve competitiveness. The current method of developing a primary logistics plan does not consider the characteristics of various transportation modes, which can lead to an increase in overall costs. The logistics system in this case has multiple transportation modes, including road, rail, and pipeline. In addition, a multiproduct pipeline has multiple injection points. For such a complex system, this work unifies the time dimension of continuous transportation by pipeline and discrete transportation by rail and road based on the idea of multimodal transportation. This paper couples a pipeline scheduling submodel and a logistics optimization submodel to construct a daily scheduling model for refined oil which is close to the actual operation in the field. This model was validated using a refined oil logistics system in China, and the results show that the optimized logistics solution decreased total transportation costs by 14.5% compared with the actual cost. This model can serve as a guide for actual operations. [ABSTRACT FROM AUTHOR]

Analyzing traffic accident data is crucial for pinpointing contributing factors, forecasting accident patterns, and informing effective safety measures. This insight leads to enhanced road safety, decreased fatalities, and better resource allocation in transportation planning. Severe weather conditions, including rain, fog, and sandstorms, can significantly reduce the visibility of highways and increase the risk of traffic accidents. The paper proposes a deep mining algorithm for road traffic accident data under adverse weather conditions. Firstly, collect traffic accident data under adverse weather conditions and complete attribute reduction, denoising, and normalization processing. Then, by improving the decision tree algorithm, the attribute selection criteria of the decision tree algorithm were enhanced; Finally, the improved decision tree algorithm is applied to achieve deep mining of traffic accident data. The experimental results show that proposed algorithm has an accuracy rate of over 95.3%, an accuracy rate of over 94.3%, a recall rate of over 92.7%, and a stable F1 value of around 0.8, demonstrating excellent performance. The provided deep mining algorithm for traffic accident data can provide more scientific and accurate decision support for traffic management departments. [ABSTRACT FROM AUTHOR]

Transshipment yards are not usually considered in optimization approaches proposed for solving the log transportation problem. However, they may provide substantive advantages with respect to the usual logistics practice of direct delivering from supply forests to mills. Therefore, this paper evaluates, at the operational level, different supply chain topologies with the purpose of assessing potential costs savings associated to the use of intermediate storage facilities. The researched problem is defined by stated sets of log-suppliers, storage and transshipment yards, and facilities demanding several types of logs over a known planning horizon. The optimal transportation and inventory plan must be found. An optimization algorithm based on Column Generation + GRASP pricing metaheuristic was developed to compute minimal operation costs associated to different supply-chain topologies over a bank of instances generated from realistic information of the forestry activity in the Argentinean province of Misiones. [ABSTRACT FROM AUTHOR]

From a socialist society where public transit was the primary form of transportation to the modern “Silicon Valley” of Eastern Europe, where the streets are flooded with cars. Cluj has changed over the years; overturned by university students for most of the year, the city and encompassing communes have experienced significant growth, which stresses the existing socialist infrastructure and causes high levels of strategic planning to accommodate the present and future transportation plans. Moreover, the CTP is no exception; as the only provider of public transportation in Cluj, it is at the centrefold of this predicament and needs to distinguish itself as a success within Cluj to provide for the community's needs. In this paper, we look at data from multiple sources to analyze what drives people's choice of transportation and pinpoint some fundamental notions as to why people choose private vehicles and other forms of transportation over public transit. Then, using SWOT and PEST analysis, we look to find solutions to benefit the community. Many options are available to develop more fluidity in the city, change the notion of public transportation, and create reliance on public transportation. [ABSTRACT FROM AUTHOR]

Many studies have been conducted worldwide to study travel behavior, specifically commuting times. Nevertheless, few or none have been conducted in the Middle East and North Africa region. The main objective of this paper is to understand the relationship between the actual, ideal and maximum willingness to commute times, and satisfaction with commuting in the city of Jounieh, Lebanon. Socio-economic and land use factors were included to investigate whether they influence commuting times. The average commuting time of the sample is 48.2 minutes, longer than their ideal commute time by about 20 minutes, while their maximum willingness to commute time is 53.46 minutes. The sample was neither satisfied nor dissatisfied with their commutes. After performing multivariate Ordinary Least Squares model, and fixing the socio-economic and land use variables, the model proved that the actual and ideal commuting times as well as the actual and maximum commuting times are positively correlated, but the actual commuting time and satisfaction are negatively correlated. A univariate Ordinary Least Square model was performed to determine the correlation between ideal and maximum commuting times; they are highly positively correlated. Finally, the average commuting times and satisfaction were compared for some categories. Women spend almost 13% less time commuting than men, yet they have lower level of satisfaction. Lebanese commuters are also shown to commute more than non-Lebanese ones by approximately 22%. University level commuters commute about 36% more than non-university level people, while unemployed and employed respondents commute for the same time. [ABSTRACT FROM AUTHOR]

This paper analyses Cork's trajectory towards a sustainable, innovative, and globally connected urban centre. On Ireland's south coast, Cork serves as an exemplar of how second-tier cities can undergo transformation and structural change, emerging as an exemplar of economic success. The paper explores the synergies between policymaking, labour markets, and transportation improvements, specifically in the pharmaceutical and IT sectors, noting their clustering and success in international markets. It explores challenges and opportunities presented by demographic shifts alongside an analysis of Cork's transport system, its port capabilities, and connectivity. The paper links to the UN SDGs and suggests strategies to promote sustainable growth and boost innovation. These enhance Cork's technological capacities, educational attainment, sustainable urban planning, and transport systems. It offers insights into urban development, focusing on place-based, future-oriented solutions and strategic directions for Cork. This study extends beyond Cork, offering a roadmap for other second-tier cities grappling with similar challenges and opportunities. It emphasises the need for comprehensive unified approaches to urban planning, aligning with global sustainability objectives, innovative standards, and best practices in city management. Cork not only pioneers its path towards resilience and innovation but also serves as a model for global second-tier cities, showcasing the growth and transformation potential of urban centres. • Cork's resilience and innovation model second-tier city transformation • Strategic FDI in Cork sets a benchmark for economic growth in similar cities. • Cork's education-labour model guides workforce development in second-tier cities. • Cork's infrastructure upgrades boost global connectivity for second-tier cities. • Inclusivity in Cork boosts economic participation and diversity in similar cities. [ABSTRACT FROM AUTHOR]

This document is an editorial from the Transport journal discussing the impact of the COVID-19 pandemic on the transport industry. The editorial highlights the significant changes that have occurred in the industry, including shifts in mobility patterns, the adoption of new health protocols, and the emphasis on sustainable transportation solutions. The document also summarizes several research papers included in the journal issue, which explore the effects of the pandemic on rail passenger traffic, public transportation usage, the transport sector in specific regions, and commuting patterns. Overall, the editorial emphasizes the need for the transport industry to rebuild public trust, embrace innovation, and promote efficiency, safety, and sustainability in a post-pandemic world. [Extracted from the article]

Transportation Planning and Technology has introduced two new features to their journal. The first is a series of articles called 'Hot Topics in Transport', where academic experts will discuss key issues and arguments related to transport planning and technology. The second feature is the 'Editor's Choice' paper, which highlights high-quality papers of interest to the transport research community. These papers will be given three months of free access and will be promoted through the journal's website and social media channels. The journal hopes that these features will enhance readers' experience and welcomes feedback on these initiatives. [Extracted from the article]

This paper investigates traffic forecasting, which attempts to forecast the future state of traffic based on historical situations. This problem has received ever-increasing attention in various scenarios and facilitated the development of numerous downstream applications such as urban planning and transportation management. However, the efficacy of existing methods remains sub-optimal due to their tendency to model temporal and spatial relationships independently, thereby inadequately accounting for complex high-order interactions of both worlds. Moreover, the diversity of transitional patterns in traffic forecasting makes them challenging to capture for existing approaches, warranting a deeper exploration of their diversity. Toward this end, this paper proposes Co njoint Spati o -Tempora l graph neural network (abbreviated as COOL), which models heterogeneous graphs from prior and posterior information to conjointly capture high-order spatio-temporal relationships. On the one hand, heterogeneous graphs connecting sequential observation are constructed to extract composite spatio-temporal relationships via prior message passing. On the other hand, we model dynamic relationships using constructed affinity and penalty graphs, which guide posterior message passing to incorporate complementary semantic information into node representations. Moreover, to capture diverse transitional properties to enhance traffic forecasting, we propose a conjoint self-attention decoder that models diverse temporal patterns from both multi-rank and multi-scale views. Experimental results on four popular benchmark datasets demonstrate that our proposed COOL provides state-of-the-art performance compared with the competitive baselines. • We explore a challenging yet practical problem: GNNs for traffic flow prediction. • Our model conjointly explores spatio-temporal relationships from both prior and posterior views. • Our model introduces a conjoint self-attention decoder that aggregates sequential representations. • Our model uses both multi-rank and multi-scale attention branches to learn representations. • Experiments on the benchmarks demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The urban public transportation hubs (UPTHs) serve as central and multimodal interchange points that provide convenient travel for city residents. Accurate prediction of the passenger flow at these hubs is crucial for balancing the travel demand with the service capacity of the hubs, which is of utmost importance for transportation planning, operations, and management. In this paper, we introduce EEMD-MST-Resnet, an integrated deep learning prediction model, incorporating ensemble empirical modal decomposition (EEMD) and multi-frequency deep spatiotemporal residual network (MST-Resnet). Firstly, we apply the EEMD algorithm to decompose passenger flow time series data into several intrinsic mode components. Subsequently, the components are evaluated using sample entropy, and the evaluated results are clustered using density-based spatial clustering of applications with noise to obtain a reconstruction plan for the components. The reconstruction process categorizes them into high-frequency, medium-frequency, and low-frequency components. Furthermore, we analyze and incorporate external factors influencing passenger travel to enhance prediction accuracy. These factors, along with the reconstructed components, are utilized in the corresponding MST-Resnet model for training and prediction. The final predictions are obtained by combining results from each component. Our experimental results reveal that our combined deep learning prediction method outperforms benchmark algorithms, including CNN, LSTM, BiLSTM, and ST-Resnet, as evident from the superior performance in RMSE, MAE, and sMAPE metrics. Remarkably, we achieve a significant improvement of over 8.225% in sMAPE. This research significantly advances accurate passenger flow prediction in UPTHs, underscoring the effectiveness of our EEMD-MST-Resnet model. The findings of this study hold substantial implications for enhancing transportation planning, operation, and management in urban settings. [ABSTRACT FROM AUTHOR]

The State of Temperature (SOT) plays a crucial role in ensuring the safety and reliability of lithium-ion batteries, as well as the stability of electric vehicles (Evs). Recently, data-driven methods for lithium-ion battery temperature estimation have often obtained short-term estimation information in a single scenario. To alleviate the above issues, this paper proposes a method for estimating the surface temperature of lithium-ion batteries based on Local and Global Trend (LGT) data augmentation and Long Short-Term Memory (LSTM) model transfer. Initially, voltage and current data are processed using two types of filtering methods to reduce noise while preserving the fluctuating characteristics of the original data. Differential features are subsequently extracted from the polynomially filtered curves. Then all the data, including the filtered data and the features are fed into the LGT data augmentation algorithm, followed by the use of an LSTM transferable model to estimate the surface temperature of the target battery. Furthermore, the method is tested on both laboratory datasets and datasets with real driving conditions, covering a wide range of environmental temperatures and driving scenarios typical of EVs. The experimental results demonstrate that retraining with only the first 30 % of the target battery's data yields effective SOT estimation results. • Utilizing voltage and current to capture the temperature characteristics. • LGT augmentation enhances data diversity and robustness in SOT prediction models. • Transferable LSTM model predicts surface temperature with limited early-stage data. • The method's performance is validated on lab and real-world driving datasets. [ABSTRACT FROM AUTHOR]

With the rapid increase in the percentage of the world's population living in cities, the design of existing transportation infrastructure requires serious consideration. Current road networks, especially in large cities, face acute pressures due to increased demand for vehicles, cyclists, and pedestrians. Although much attention has been given to improve traffic management and accommodate the increased demand via coordinating and optimizing traffic signals, research focused on adapting the static allocation of street spaces and right-of-way dynamically based on mixed traffic flow is still scarce. This paper proposes a multi-agent reinforcement learning (RL) agent approach that cooperatively adapts the individual lane widths and right-of-way access permissions based on real-world mixed traffic flow. In particular, multiple cooperative agents are trained with mixed temporal data that learn to decide suitable lane widths for motorized vehicles, bicycles, and pedestrians, along with whether co-sharing space between pedestrians and cyclists is safe. Using a microscopic traffic simulator model of a four-legged intersection, we trained our RL agent on synthetic data, and tested it on realistic multi-modal traffic data. The proposed approach reduces the overall average waiting time and queue length by 48.9% and 37.7%, respectively, compared to the Static (baseline) street design. Additionally, we observe CALM's ability to gradually adjust lane widths, contrasting with the Heuristic implementation's erratic lane adjustments, which pose potential safety concerns. Notably, the model learns to adaptively toggle the co-sharing of street space between cyclists and pedestrians as one co-shared lane, ensuring comfort and maintaining the level of service according to the designer's policy. Finally, we demonstrate CALM's scalability on a simulated large-scale traffic network. • We propose a decentralized MARL algorithm suitable for large-scale networks. • Our method is robust under extreme traffic surges and generalizes to new scenarios. • Our approach fairly adapts street space to dynamically changing mixed traffic flow densities. [ABSTRACT FROM AUTHOR]

• We present an experiment investigating pedestrians' responses to knife-based attacks. • The experiment uses financial incentives and an 'aggressor' to recreate a knife-based terrorist attack. • The results show that participants were stressed and reported fear during the experiment. • Both social influences and demographics had an impact on participants' response. Terrorist attacks have become both more frequent and deadlier in recent decades. Knife attacks by terrorists in particular are both high impact and relatively frequent. Understanding how individuals move when confronted by an attacker can save lives by informing the planning of transportation hubs, entertainment venues and other spaces where large numbers of people congregate. Using pedestrian dynamics to predict responses to such attacks has the potential to further improve the survival of those involved by providing insights to emergency service responders when a terrorist situation has occurred in a crowded venue. However, given the lack of appropriate data, it is currently impossible to accurately predict pedestrian movement responses to terrorist attacks. This paper describes a practical study that developed a methodology and implemented a set of experiments examining responses to knife-based attacks by an unexpected and hostile individual. The experiments used financial incentives and an 'aggressor' to recreate a knife-based terrorist attack in an ethically viable setting. The participants of this study (n = 80) were tracked using ultra wide-band sensors to provide temporal and spatial positional data in relation to the attacker and to each other. Participants subsequently completed a questionnaire to report their psychological response during the experiment, thus allowing psychological perception and movement responses to be compared. The results show that participants were stressed and reported fear during the experiment, indicating that the experimental approach is a potentially valid proxy for a real-life attack. The analysis highlights that participants' decisions to stay or run away were strongly influenced by the actions of other nearby participants, while participants' continuous movement response (movement speed) was highly dependent on relative position to the aggressor. Participant demographics were a factor with female participants less likely to move and slower moving, while older participants were also slower moving. From these results we have been able to construct a model of how pedestrians may react when faced with a sudden attack. We discuss our findings and their potential to contribute to emergency planning and response, finally we comment on opportunities for further study. [ABSTRACT FROM AUTHOR]

Traffic flow prediction is a critical component of Intelligent Transportation Systems (ITS). However, the dynamic temporal variations in traffic flow, especially in potential occurrence of unexpected incidents, pose challenges to the prediction of traffic flow. This paper proposes a Spatial–temporal Gated Attention Transformer (STGAFormer) model based Graph Neural Network(GNN), leveraging the encoder architecture of the transformer. The gated temporal self-attention in the model, a novel module, can improve the model's ability to make long-term predictions and handle sudden traffic incidents by enhancing the extraction of both local and global temporal features. Additionally, this paper proposes a distance spatial self-attention module to extract spatial features, which employs thresholding to selectively identify crucial features from both nearby and distant regions. In this way, the model's ability to assimilate critical spatial information is promoted. Moreover, our model incorporates a diverse range of inputs, including traffic flow attributes, periodicity, proximity adjacency matrix, and adaptive adjacency matrix. Experiments from four real datasets demonstrate that STGAFormer achieves state-of-the-art performance, especially the MAE value of the PeMS08 dataset experiment is improved by 3.82%. This method offers valuable insights and robust support for future transportation planning. [Display omitted] • The proposed model achieves the PeMS of state-of-the-art. • The designed Graph Neural Network with transformer excels in traffic prediction. • Multidimensional embedding method fuses spatial–temporal features effectively. • The gated temporal self-attention capture both local and global temporal feature. [ABSTRACT FROM AUTHOR]

Improving public transport accessibility (PTA) has been considered as an effective measure for promoting sustainable urban development. Based on the grid-level data in Nanjing, China, this paper explores the spatially heterogeneous effects of PTA on road traffic CO 2 emissions using a geographically weighted random forest (GWRF) model. A simulation-based counterfactual analysis framework is further proposed to predict the intervention effects of improving PTA. Two kinds of practical interventions, adding facilities and increasing service frequency, are considered in our counterfactual prediction. The effects of improving PTA across different areas are predicted and compared. Our results indicate that the GWRF model with a properly tuned bandwidth outperforms the conventional random forest model. The results of counterfactual analysis show that improving PTA could achieve greater environmental benefits in suburb areas. With the process of urbanization in Nanjing, the population and economy has grown rapidly in suburb areas. Therefore, it is reasonable to improve public transport services in these areas. Based on our findings, PTA has potential to make a significant contribution to sustainable development of urban transportation. Moreover, our findings with respect to heterogeneous effects likely improve the efficiency of local transport policies that target such areas, helping achieve greater environmental benefits. [ABSTRACT FROM AUTHOR]

Accurate shared micromobility demand predictions are essential for transportation planning and management. Although deep learning methods provide robust mechanisms to tackle demand forecasting challenges, current models based on graph neural networks suffer from limited scalability and high computational cost. There is both a need and significant potential to enhance the accuracy and efficiency of existing shared micromobility demand forecasting models. To fill these research gaps, this paper proposes a deep learning model named Interactive Convolutional Network (ICN) to forecast spatiotemporal travel demand for shared micromobility. The proposed model develops a novel channel dilation method by utilizing multi-dimensional spatial information (i.e., demographics, functionality, and transportation supply) based on travel behavior knowledge for building the deep learning model. We use the convolution operation to process the dilated tensor to simultaneously capture temporal and spatial dependencies. Based on a binary-tree-structured architecture and interactive convolution, the ICN model extracts features at different temporal resolutions and then generates predictions using a fully-connected layer. We conducted two practical case studies from Chicago, IL, and Austin, TX to test the proposed model. The results show that the ICN model significantly outperforms all benchmark models. The model predictions have the potential to assist micromobility operators in developing efficient vehicle rebalancing strategies, while also providing cities with guidance on enhancing the management of their shared micromobility system. [ABSTRACT FROM AUTHOR]

Global synchromodal transportation is a promising strategy for providing efficient, reliable, flexible, and sustainable container shipping services across continents. It involves integrating multiple modes and routes owned by various operators to create a comprehensive transport plan. However, these operators often have their own local networks and are hesitant to cede control to a centralized platform. Instead, they prefer to share limited information in a coordinated manner to achieve a common goal without sacrificing their own benefits. This paper proposes a coordinated mechanism for global synchromodal transport planning, in which a global operator proposes incentives to local operators to select the most efficient modes and routes for shipping containers from one continent to another. An augmented Lagrangian relaxation approach is developed for the global operator to generate incentives, and a heuristic algorithm is designed to address the computational complexity of the optimization problems faced by local operators. We incorporate the proposed approaches with a rolling horizon framework to handle dynamic shipment requests received from spot markets and with a buffer strategy to address travel time uncertainties. The coordinated mechanism is tested on a real network between Asia and Europe, and results show that it can significantly increase total profits, reduce request rejections, and reduce infeasible transshipments compared to decentralized global transportation plans currently in use, particularly under scenarios with higher degrees of dynamism and uncertainty. • A coordinated approach is proposed for global synchromodal transportation. • Both vertical and horizontal collaboration among multiple operators are considered. • A rolling horizon framework and a buffer strategy are designed. • The centralized, coordinated, and decentralized approaches are compared. • Real networks between Europe and China are used to conduct extensive experiments. [ABSTRACT FROM AUTHOR]

The safety concerns for autonomous vehicles (AV) are shown to be a roadblock to their adoption. This paper addresses these concerns by studying a unified, game-theoretic framework (leader–follower game) of mixed traffic in which AVs and human-driven vehicles (HV) coexist, with endogenous vehicle demand and different types of accidents emerging in mixed traffic as crucial building blocks. We study the interaction between three types of players: (i) a policymaker, who decides on the liability regime and the level of V2I connectivity infrastructure, (ii) an AV producer, who decides on the AV price and safety level, and (iii) consumers, who differ in their preference for each vehicle type and choose the one they like best. Using both analytical and numerical tools, we analyze how the two policy variables, liability and V2I connectivity, affect behavior on the demand and supply side of the vehicle market and, in turn, AV market penetration and overall road safety. We also characterize optimal policies, thereby taking into account the market participants' behavioral responses. Our findings provide guidance for a fast adoption of AVs and a smooth transition from existing traffic conditions to a mixed traffic environment, and assist in decision making for policymakers, legal agencies, traffic operation and transportation planning agencies, as well as car manufacturers. • Game-theoretic framework of mixed traffic with autonomous and human-driven vehicles • Endogenous AV demand and interaction between policymaker, AV producer and consumers • Focus on (product) liability and V2I connectivity as key policy variables • A higher level of AV safety might reduce the demand for AVs • Stringent AV liability induces high safety investment but low AV market penetration [ABSTRACT FROM AUTHOR]

In this paper, we present a multi-objective low-carbon multimodal transportation planning problem with fuzzy demand and fuzzy time (MOLCMTPP-FDFT) that minimizes both cost and time while incorporating mandatory carbon emission, carbon tax, carbon trading, and carbon offset policies. Chance constrained programming and interval programming are introduced to formulate the fuzzy demand chance constrained programming model and the fuzzy time interval programming model, respectively. Then, the mathematical model of uncertain MOLCMTPP-FDFT is transformed into a deterministic model. Based on the sparrow search algorithm, t distribution and the concept of Pareto optimality for multi-objective optimization, we also propose a solution strategy for the proposed model. In this algorithm, the number of iterations is used as the degree of freedom of - to update the sparrow location, which strikes a balance between the capabilities of global search and local search. Finally, the proposed algorithm and MOLCMTPP-FDFT are applied to a real case, resulting in a minimum cost of 260730.48 and a time duration of 13.044, which outperform the minimum cost of 268874.88 and minimum time of 18.32 obtained using single-mode transportation. The carbon emissions resulting from the lowest-cost solution obtained using single-mode transportation are 3,198.48, which are significantly more than the allowed emissions. Therefore, the proposed algorithm and mathematical model of MOLCMTPP-FDFT are valuable tools for optimizing multimodal transportation route. Additionally, the experimental results not only validate the superior efficiency and energy-saving benefits of the proposed multimodal transportation routes in comparison to the actual single-modal transportation, but also demonstrate the applicability of different low-carbon policies. [ABSTRACT FROM AUTHOR]

Environmental justice – the concept that environmental amenities and burdens should be shared equitably among all races and socioeconomic classes – has gained diplomatic traction internationally, and political traction domestically in the United States, in recent years. In this paper, we focus on developing federal policy relevant to transportation project planning and operation in the U.S.; however, these developments reflect international issues of inequity associated with climate change and environmental burdens. First, we provide an overview of federal policy initiatives related to environmental justice, including President Biden's Justice40 initiative. Then, we examine the U.S. National Environmental Policy Act (NEPA) as a potential avenue for mandating environmental justice assessment in the transportation industry, through the lenses of both procedural and distributive justice. Finally, we review several publicly available online Geographical Information System (GIS) tools that were recently developed for purposes of expanding public and governmental understanding of environmental justice challenges, and provide decision support for users to incorporate these tools into the environmental impact assessment process for transportation projects. [ABSTRACT FROM AUTHOR]

• We developed a distributionally robust optimization (DRO) model that takes into account the train operation and maintenance costs, and the robustness of the train timetables and maintenance windows. • We introduced an innovative approach to reconstructed the DRO model using a multi-commodity network flow framework. We designed a decomposition mechanism based on the alternating direction method of multipliers (ADMM) to dualize the cross-resolution consistency constraint and track capacity constraints. Further, we linearized the quadratic penalty items, thereby decomposing the original problem into a series of sub-problems. • We developed an ADMM-driven and nested simulation heuristic algorithm aligned with the problem's characteristics. The algorithm involves variable penalty parameters, with the delay costs of the arcs being iteratively updated based on simulation. • We verify the quality and efficiency of the proposed algorithm using a series of cases based on actual data. This research paper focuses on the optimization of train timetables and maintenance windows, both of which significantly impact service quality and cost-effectiveness. Uncertainties in both elements can disrupt established transportation plans, causing train delays and maintenance cancellations. Accordingly, we highlight the necessity of augmenting the robustness of these schedules. In this study, we explored an integrated robust optimization of maintenance windows and train timetables using a distributionally robust optimization (DRO) model. The DRO model was established with two types of binary variables and a cross-resolution consistency constraint was introduced to couple them. We innovatively employed a multi-commodity network flow framework to reconstruct the DRO model and designed an alternating direction method of multipliers (ADMM)-based decomposition mechanism. This mechanism was applied to dualize the cross-resolution consistency and track capacity constraints. To handle the problem, we developed a heuristic algorithm driven by ADMM, along with a nested simulation. The algorithm's effectiveness is demonstrated through numerical experiments. [ABSTRACT FROM AUTHOR]

Road networks in the past are imperative for understanding evolution of transportation infrastructure, urban sprawl, and route planning, etc. Various approaches have been developed for road extraction from historical maps, among which deep learning techniques stand out as the most effective ones. However, little attention has been paid to investigating road vectorization and classification from historical maps. Moreover, road classification via machine learning methods usually requires large amounts of dedicated training data. To address these issues, this paper proposes a novel and comprehensive framework for road vectorization and classification on the basis of road segmentation from historical maps. First, deep learning is used to get pixel-wise raster road segmentation results, which are further skeletonized using morphological operations. Then, considering that each road class is represented with a certain symbol, a painting function is defined for each class able to paint the corresponding symbol. These painting functions are then used to draw road segments along the skeletons. Since the start and end points in each painting function are used to vectorise the segment, this method achieves vectorization and classification at the same time. Our method is validated on four Siegfried map sheets in Switzerland, and evaluated via both visual and quantitative assessments. The results indicate that the method is capable of classifying roads accurately. In particular, two evaluation metrics completeness and correctness achieve 90.69% and 72.71% respectively for road class 2 which accounts for the highest portion in the map. Moreover, the results of this method avoid the saw-toothed issue of vectorised road lines. This research is beneficial for creating complete vector road network datasets with class information to support decision-making in urban planning and transportation. • A novel framework is proposed to vectorise and classify roads from historical maps. • Deep CNNs are utilized to systematically and accurately extract roads. • Road classification and vectorization are achieved simultaneously by symbol painting. • Visual and quantitative assessments validate the effectiveness and robustness of the framework. [ABSTRACT FROM AUTHOR]

This paper advances theories of public administration, geographies of policy, and transportation planning via an analysis of 17 in-depth interviews conducted with transportation policy elites in the State of New Jersey (USA). In the case studied, these elites sought to facilitate the public's transition to electric vehicles, reflecting a policy change initiated by the state government. However, they were affected by organizational traps that existed beyond the bureaucratic institution in a constellation of related field activities. Using qualitative analysis techniques, the data revealed that organizational traps diffuse alongside policy actors as they themselves sought to diffuse policy. The result revealed barriers that kept public engagement relatively circumscribed, even as elite policy makers believed they were achieving their policy goals via participation of the communities that they intended to serve. [ABSTRACT FROM AUTHOR]

Optimal clean process design requires strict constraints to enforce waste and byproduct management, all of which can be formulated in the language of mathematical programming. However, waste management and the utilization of by-products are often carried out in locations or periods other than the production process. The paper describes all modeling steps by P-graphs sufficient to represent raw material availability and production capacities in multiple time periods at multiple sites, as well as transportation and storage capacities of process materials and wastes. These steps are integrated into a single comprehensive model generation algorithm. For easier understanding, each model generation step is illustrated by a case study of planning a multi-site multi-period furniture production process alongside the recent challenges of energy supply and waste management. Finally, the case study of furniture production is analyzed under various circumstances to highlight the power of the proposed tools in daily production and transportation planning. Accordingly, the proposed method provides such alternative 5 best manufacturing and logistics plans that, in the event of a complete failure or overloading of one of the production capacities at either locations, there is still an alternative plan within a 3% profit decrease. • Optimization model is proposed for multi-site multi-period clean process design. • The mathematical model force waste and byproduct management. • Model generator algorithm is provided for the model generation. • Model generation is illustrated by a 3-day multi-site furniture manufacturing. • P-graph based solution procedure results in N-best alternative production plans. [Display omitted] [ABSTRACT FROM AUTHOR]