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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 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]

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]

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]

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]

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]

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]

The article discusses the goal in publishing papers is to make useful contributions to the state of knowledge and practice in transportation engineering. It mentions that the papers receiving at least one award-quality designation in the review process are considered; winners generally have multiple award-quality designations; and the role of the referees in selecting the best papers as well as in enhancing the quality of the journal is absolutely critical.

Starting in the 1960s, traffic congestion became a staple part of Manila's history. Rapid urbanization, century-old thoroughfares, and large-scale migrations to Manila are just some of the reasons for the worsening traffic conditions. During the Marcos Era, the academe, practitioners, government agencies, and various organizations produced plans, position papers, and transport documents that sought to understand, assess, and perhaps provide possible solutions. Using data available in the libraries of University of the Philippines Diliman, this research note seeks to provide an initial survey and preliminary review of transport materials that documented and proposed solutions to the traffic congestion in the city of Manila from the 1960s until the 1980s. [ABSTRACT FROM AUTHOR]

The article offers information on goal in publishing papers is to make useful contributions to the state of knowledge and practice for transportation engineering. Topics include the papers receiving at least one award quality designation in the review process are considered like winners generally have multiple award quality designations, and the role of the referees in selecting the best papers as well as in enhancing the quality of the Journal is absolutely critical.

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]

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]

In recent years, due to the further development of the market economy, the internal competition in the large-cargo transportation industry has become increasingly fierce, and the profit space has been greatly compressed. Therefore, large-cargo logistics enterprises are paying more and more attention to the research of highway transportation route plan. The highway transportation scheme selection is looked as the multi-attribute decision-making (MADM). In this paper, the triangular fuzzy neutrosophic numbers (TFNN) grey relational analysis (TFNN-GRA) method is established based on the classical grey relational analysis (GRA) and triangular fuzzy neutrosophic sets (TFNSs) with completely unknown weight information. In order to obtain the weight values, the information Entropy is established to obtain the weight values based on the score and accuracy functions under TFNSs. Then, combining the traditional fuzzy GRA model with TFNSs information, the TFNN-GRA method is set up and the computing steps for MADM are established. Finally, a numerical example for highway transportation scheme selection was established and some comparisons are established to study the advantages of TFNN-GRA. The main contributions of this paper are established as follows: (1) the information Entropy is established to obtain the weight values based on the score and accuracy functions under TFNSs; (2) the TFNN-GRA method is established with completely unknown weight information. (2) the TFNN-GRA method is established and the computing steps for MADM are established. (3) Finally, a numerical example for highway transportation scheme selection was established and some comparisons is employed to study advantages of TFNN-GRA method. [ABSTRACT FROM AUTHOR]

In order to optimize the allocation of flight slot resources and improve the regularity of civil aviation flights， the method of season division under the level of large-scale air traffic operation is studied in this paper. The monthly time series similarity of air traffic operation efficiency index is analyzed by the dynamic time warping algorithm， an optimized flight season division model is established， and the fast non-dominant sorting genetic algorithm with elite strategy is used to solve the problem. The TOPSIS method with entropy is used to evaluate the Pareto solution， and the final flight season division results under different division angles are given. Then and the effects of season division results and weight setting on the results under different air traffic operation levels are analyzed. The results show that， in the case of comprehensively considering the influence of the evaluation index of air traffic operation level， the result of optimal flight season division is stable with the change of weight. The method proposed in this paper is not only suitable for the flight season division of large-scale airspace environment， but also suitable for exploring the flight season mode of individual airport operation， thus providing a means for the Civil Aviation Administration and the airport to make flight schedule scientifically. [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]

In recent years, the application of semantic segmentation methods based on the remote sensing of images has become increasingly prevalent across a diverse range of domains, including but not limited to forest detection, water body detection, urban rail transportation planning, and building extraction. With the incorporation of the Transformer model into computer vision, the efficacy and accuracy of these algorithms have been significantly enhanced. Nevertheless, the Transformer model's high computational complexity and dependence on a pre-training weight of large datasets leads to a slow convergence during the training for remote sensing segmentation tasks. Motivated by the success of the adapter module in the field of natural language processing, this paper presents a novel adapter module (ResAttn) for improving the model training speed for remote sensing segmentation. The ResAttn adopts a dual-attention structure in order to capture the interdependencies between sets of features, thereby improving its global modeling capabilities, and introduces a Swin Transformer-like down-sampling method to reduce information loss and retain the original architecture while reducing the resolution. In addition, the existing Transformer model is limited in its ability to capture local high-frequency information, which can lead to an inadequate extraction of edge and texture features. To address these issues, this paper proposes a Local Feature Extractor (LFE) module, which is based on a convolutional neural network (CNN), and incorporates multi-scale feature extraction and residual structure to effectively overcome this limitation. Further, a mask-based segmentation method is employed and a residual-enhanced deformable attention block (Deformer Block) is incorporated to improve the small target segmentation accuracy. Finally, a sufficient number of experiments were performed on the ISPRS Potsdam datasets. The experimental results demonstrate the superior performance of the model described in this paper. [ABSTRACT FROM AUTHOR]

Connected vehicle (CV) data in this paper refer to the in-vehicle telematic data, including trajectories and driving events (e.g., hard braking) collected by vehicle manufacturers when vehicles are moving. Recently manufactured vehicles are equipped with cellular modems and Internet of Things (IoT) devices to collect vehicle data. Such data, after removing personal information, are being redistributed to third-party organizations. Compared to other probe vehicle data, the CV data has a higher penetration rate, ubiquitous coverage, and almost lane-level positioning accuracy. These features pave the road for novel transportation applications in transportation planning and traffic operations. In this paper, we represent a novel framework to estimate the regional link volumes based on the CV data and a deep neural network (DNN) model. The training data are generated according to the link volumes (targeted model output) and the corresponding CV counts (input features) at the same locations. The DNN model's performance was compared with other estimation methods like linear regression and random forest and showed superior performance. The trained DNN model takes ubiquitous CV counts from other locations to estimate the corresponding link volumes. As a case study, the proposed DNN model was trained with a large training data set derived from CV data and time-dependent link counts collected at over 1,200 locations on freeways in the Dallas FortWorth, Texas, area. The results reveal good accuracy and robustness. [ABSTRACT FROM AUTHOR]

The usefulness of transportation project evaluation depends on the completeness of its benefit measures. Since transportation networks are intrinsically spatial, transportation improvement projects have spatial access and location characteristics that can lead to a variety of non-user economic benefits. Recent research has enabled us to better understand how spatial context and spatial heterogeneity play further roles in generating efficiency gains for non-users, in the form of productivity, income, and cost savings for both private and public sectors of the economy. This paper draws upon that body of research to expand our understanding of the means by which transportation projects can generate economic efficiency gains, and approaches needed to measure them. It covers topics beyond those captured by current definitions of "wider economic benefits," including additional sources of scale economies associated with freight distribution and connectivity, and further public and private sector economic gains enabled by environmental and social inclusion improvement. It points to ways that non-user economic benefits can be more comprehensively defined and better measured by recognizing their spatial scale, context, and threshold effects. It also identifies ways that current benefit measurement methods introduce unintended bias into transportation investment decision-making through omission and mismeasurement. The result is a case for a refresh of thinking about how we classify and recognize non-user economic benefits in transportation evaluation, and how we apply transportation planning and economic models to support their measurement. • Transport decisions biased when non-user benefits are ignored or mis-measured. • Non-user economic benefits depend on spatial access, proximity, or connectivity. • Spatial scale, context, and heterogeneity affect measurement of non-user benefit. • Benefits from freight density, virtual density, effective density, non-user cost. • Rigid travel modeling standards hold back non-user benefits. [ABSTRACT FROM AUTHOR]

For the low-carbon multimodal transportation planning problem with fuzzy demands and carbon emission limit, this paper proposed a multi-objective multimodal transportation mathematical model to minimize the path cost and carbon emission. Firstly, it used the chance constrained programming to deal with the uncertain demand represented by trapezoidal fuzzy number according to the characteristics of the model. Secondly, it designed the path relinking algorithm, multiple crossover operators, and mutation operators to replace the search process of original Harris hawks optimizer algorithm. It successfully applied the algorithm to the discrete optimization problems on the premise of preserving the original characteristics of this algorithm. Finally, it studied the multimodal transportation from Nanning city to Harbin city as a case, which gave multiple reasonable route scheme. HHHO was compared with other algorithms. The results show that HHHO, NSGA-Ⅱ, GA, SA and PSO all obtain a set of near-optimal solutions with five solutions in an ideal time, and the solution set of HHHO is closer to the optimal solution set. The running time of HHHO and the other four algorithms are 86.50 s, 118.26 s, 101.67 s, 81.22 s and 68.40 s, respectively. HHHO is faster than GA and NSGA-Ⅱ in running time. Therefore, these results verify the feasibility of the model and the effectiveness of hybrid Harris hawks optimizer algorithm. [ABSTRACT FROM AUTHOR]

Analysis of urban area function is crucial for urban development. Urban area function features can help to conduct better urban planning and transportation planning. With development of urbanization, urban area function becomes complex. In order to accurately extract function features, researchers have proposed multisource data mining methods that combine urban remote sensing and other data. Therefore, the research of efficient multisource data analysis tools has become a new hot topic. In this paper, a novel urban data analysis method combining spatiotemporal wireless network data and remote sensing data was proposed. First, a Voronoi-diagram-based method was used to divide the urban remote sensing images into zones. Second, we combined period and trend components of wireless network traffic data to mine urban function structure. Third, for multisource supported urban simulation, we designed a novel spatiotemporal city computing method combining graph attention network (GAT) and gated recurrent unit (GRU) to analyze spatiotemporal urban data. The final results prove that our method performs better than other commonly used methods. In addition, we calculated the commuting index of each zone by wireless network data. Combined with the urban simulation conducted in this paper, the dynamic changes of urban area features can be sensed in advance for a better sustainable urban development. [ABSTRACT FROM AUTHOR]

With the development of smart mobile devices and global positioning technology, people's daily travel has become increasingly dependent on online car-hailing. Meanwhile, it has also become possible to use multi-source data to explore the factors influencing urban residents' car-hailing trips. Using online data on car-hailing trajectories, points of interest (POIs) data and other auxiliary data, the paper explores how the built environment impacts online car-hailing passengers. Within a 200 x 200m research grid, the unique spatiotemporal patterns of weekday car-hailing trips during a one-week period are analyzed, using statistics on pick-ups and drop-offs at different time of the day. By combining these data with built environment variables and various economic and traffic indicators, a multi-scale geographically weighted regression (MGWR) model is developed for different time scales. The MGWR model outperforms the classical geographically weighted regression (GWR) model and the ordinary least squares (OLS) regression model in terms of goodness of fit and all other aspects. More importantly, this study finds a high degree of temporal and spatial heterogeneity in the impact of built environment factors on local car-hailing trips across different regions, and the paper analyzes the business residence coefficient in detail. The study provides valuable insights to help improve the level of urban transportation services, as well as urban transportation planning and construction. [ABSTRACT FROM AUTHOR]

This paper proposes a collaborative optimization method for transportation planning and land use. This method combines system dynamics models, structural equations, DEA evaluation and BP neural network technology to construct a complete transportation planning and land use collaborative optimization system. In this system, system unit determination is a key problem, which has a different relationship with each stage. This paper uses the DEMATEL model to construct a causality diagram and an incidence matrix for the system units, to identify the central influencing unit and causal influencing unit of the method of collaborative optimization of transportation planning and land use. While simplifying the model through the DEMATEL model, the macro interaction mechanism between urban traffic and land use will be clarified. Further, the extracted key influence units can be micro-decomposed to construct subsystems of micro-elements, which makes preliminary preparation for the analysis of static and dynamic integration model. [ABSTRACT FROM AUTHOR]

The paper moves from the scientific literature on Road Ecology and Railway Ecology to merge related concepts and criteria under a single definition: Ecology of Transportation Infrastructure (ETI). Common traits are the negative impacts generated by road and rail infrastructure on natural ecosystems, resulting in habitat loss or fragmentation for flora and fauna. Although not much addressed within transportation studies, the ETI contribution is crucial when addressing the enforcement of regulations in the fields of strategic environmental evaluation for transportation projects and planning, with a focus on landscaping issues. To this end, evaluation criteria are proposed to meet the regulations requirements, along with the main measures to implement. The goal is to increase awareness among transportation researchers and operators of the need to introduce ETI in studies and practice on construction, maintenance and evaluation of railway infrastructures, as a criterion to meet the environmental requirements. Thus, the paper is structured as follows: the development of the fundamental concepts and the related literature review is reported (Section 1) and the relevance within the regulatory tools on environmental assessments at landscape scale is stressed along with some factors of complexity affecting the process (Section 2); eventually, ETI criteria and measures to implement ETI with a focus on railways lines and artifacts are described (Section 3), and guidelines to assess their effectiveness within planning decision-making processes are proposed (Section 4). [ABSTRACT FROM AUTHOR]

Traffic analysis zone (TAZ) is the basic unit of urban transportation planning. The appropriateness of TAZ delineation will affect the rationality of transportation planning and the accuracy of transportation analysis, and thus affect the final planning decision-making. However, in the big data era, there is still a lack of methods to integrate multi-source data for TAZ delineation. How to effectively fuse multi-source heterogeneous data in the TAZ delineation process is still an unsolved technical difficulty. To fill this gap, this paper designs a multi-source data-driven stepwise strategy to solve the TAZ delineation problem by creating a zoning system with zones showing homogeneous mobility behaviors and containing homogeneous land use characteristics. Firstly, mining the spatial-temporal travel features and land use information of transit stations and parcels from multi-source data, including transit smart card data, ride-hailing data, bike-sharing trip data, and point-of-interest (POI) data. Then, a core parcel determination algorithm which mainly consists of Fuzzy C-Means station clustering and a constructed parcel core degree function is proposed to generate the core parcels of each potential TAZ. After that, parcels are clustered around the core ones into TAZs through a multi-feature driven clustering algorithm, in the process of which the homogeneity within TAZs is guaranteed. Finally, the optimal zoning system is obtained by comparing the information loss calculation result of multiple zoning schemes. Taking Beijing as a case study area, 624 TAZs are obtained by applying the proposed method. The delineation result is comparatively analyzed with TAZs obtained by the traditional delineation method and a baseline method by applying multi-indicator measurement. The result reveals that the presented approach can promote the rationality of TAZ delineation. • A stepwise delineation model is proposed, which can fuse multi-source heterogeneous data and generate TAZs by data-driven. • The method ensures the optimal homogeneous zoning results in terms of mobility behaviours and land use within each zone. • The effectiveness of the delineation method is verified by taking Beijing as a case study area. • This paper provides innovative ideas on how to integrate multi-source big data to improve transportation planning. [ABSTRACT FROM AUTHOR]

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]

Park-and-Ride (P&R), referring to the transfer between private cars and public transportation, is a public policy and travel mode that advocates green travel to reduce traffic congestion in big cities. Through an analysis on the development and focus of P&R research in China and abroad, this paper examines the research progress and explores the research trend on P&R from the perspective of travel mode choice, transfer demand forecast, location planning, operational management, and benefit evaluation. It points out that, compared with the research abroad, the P&R research in China is insufficient on the subjects of transportation planning and benefit evaluation. It proposes that, in future, research should focus more on planning, management, and benefit evaluation of P&R, so as to enhance the intelligent management of transportation system and promote the implementation of policies supporting P&R development. [ABSTRACT FROM AUTHOR]

Accurate turning movement counts (TMCs) data collected from regional-wide signalized intersections is critical to regional transportation planning and simulation modeling. A variety of existing traffic sensors, configured at intersections for traffic detection and signal control, can generate a large amount of real-time high-resolution event-based data from traffic controllers but few of these sensors are configured to collect TMC. This paper proposes a methodology for estimating network-level TMC using existing traffic controller event-based data without installing additional sensors. First, relevant features that can indicate traffic arrival are extracted from existing event-based data, including detector occupancy time, detector-triggered count, and green time duration. With these features, a multi-output multilayer neural network model is developed to estimate TMC. To further improve network-level TMC estimation accuracy, intersection infrastructure data and point-of-interest (POI) data are included as exogenous variables for the proposed model. Ninety-three signalized intersections are chosen from the Pima County region, Arizona, to calibrate and verify the developed model. The validation results show that the proposed model can accurately estimate TMC, as indicated by a median Root Mean Square Error (RMSE) of 41 veh/15 min, 11 veh/15 min, and 12 veh/15 min for through movement, left-turn movement, and right-turn movement volume estimation, respectively. This research provides a new possibility of utilizing existing data sources to obtain network-level TMC data without additional infrastructure and labor costs for transportation agencies. [ABSTRACT FROM AUTHOR]

The traffic is one of the major issues faced by people in almost every city. Predicting the traffic has always been very challenging for transportation planning as well as the city manager. With the growth of population and usage of number of vehicles has increased the need for reliable traffic prediction system. The system should be capable of reducing congestion, avoiding the accidents, and optimizing the traffic flow that enhances road safety. The use of machine learning algorithms wouldbemoresuitableinhandlingthetrafficandmaintainingtheflowofvehicles. The paper focus on the work related to handle the traffic with the use of machine learning principles such as LSTM and Random Forest (RF) algorithm are applied. The advanced online dataset for traffic forecasts is used. The system is tested to have improved accuracy with the help of feature engineering. The basic dataset had the following fields: Vehicle Id, Time, Date, Junction Id are the primary and significant parameters considered to anticipate the traffic at each zone. With this we have added the day and weekend component to enhance our result. [ABSTRACT FROM AUTHOR]

A low-cost, timely, and durable long-term approach to resident travel surveys is crucial for authorities to understand the city's transportation systems and formulate transportation planning and management policies. This paper summarizes commonly used wireless positioning technologies and uses the STDBSCAN method to identify travel endpoints based on the characteristics of trajectory location information. It uses Shenzhen cellular signaling data to visually analyze the spatial and temporal distribution of urban traffic demand, traffic correlation, and asymmetry of traffic flow between different traffic zones. The results confirm that mobile internet information represented by cellular signaling information can effectively reflect the traffic status of urban areas, which, compared to traditional travel survey methods, has the advantages of lower cost, more timely feedback, and can be durably carried out in the long term. [ABSTRACT FROM AUTHOR]

The characteristics of intersection crashes are not only affected by the subject intersection where the crash occurs but also are correlated with environmental conditions of neighboring analysis zones. There are few studies on intersection crash analysis to solve certain spatial effects on microscopic safety issues by proactively incorporating highway safety improvement measures into the long-term transportation planning process. The objective of this paper is to develop a heuristic traffic safety analysis system where spatial spillovers analysis is integrated into roadway safety assessment to incorporate micro variables and macro variables. With K-means clustering technique in a GIS environment, 8 hotspot counties are identified from 88 counties in Ohio, which have high intersection crash propensity. The rest of counties are identified as general counties. Then, an innovative integrated Generalized Linear Model is adopted to identify 11 and 20 significant variables that contribute to the intersection crash propensity in hotspot counties and general counties, respectively. To verify compatibility of intersection crash frequency models with macro-level and micro-level measurement, Reading Road in Cincinnati, Hamilton County (hotspot county) and I-71 in Mason City and Lebanon City of Warren County (general county) are used as examples for the test, and the results show a good consistence. [ABSTRACT FROM AUTHOR]

With the rapidly increasing number of older people, vehicle dependence in countries like the U.S. poses many challenges for transportation planning. Vehicle dependence among most Americans has substantial environmental, safety, and equity implications, and it excludes those with limited ability to drive from participating in many everyday social activities. Also, an age-based digital divide weakens the potential role of transportation technologies and information and communication technologies in reducing travel difficulties for older people. Nevertheless, current federal policies do not sufficiently address the transportation challenges facing an aging population. This paper provides an agenda for sustainable and equitable transportation in an aging society through various modes of transportation and enhanced interagency coordination and collaboration. [ABSTRACT FROM AUTHOR]

Traffic planning and management is an important task for governments to achieve a proper functioning of the cities. The configuration of road signs and traffic lights is part of this process. The selection of an appropriate configuration is modeled as a variant of the traffic light cycle optimization problem (TLCOP), which is NP-Hard. To evaluate the impact of each configuration, agent-based simulations can be performed. Based on the importance and complexity of the problem, the objective of this paper is to present and evaluate a metaheuristic scheme to solve it and analyze solutions obtained. The algorithms were applied to six zones of Havana, Cuba. Among all the algorithms used, the genetic algorithm obtained the best performance. [ABSTRACT FROM AUTHOR]

Traffic patterns and related problems are changing due to population growth, calamities, among others. Transportation planning is crucial in finding solutions to these problems. Travel forecasting models such as activity-based demand and agent-based microsimulation ones are effective tools to simulate and evaluate transportation planning strategies. This paper aims to develop a framework in line with the advances and concepts from previous studies on transportation demand modelling. Numerous research from various countries in the past years that examine recent developments in agent-based microsimulation and activity-based travel demand models are investigated here. The integration of these models in the context of various scenario types are discussed. The findings and framework are envisaged to aid in the development of a microsimulation model that integrates both activity- and agent-based models. [ABSTRACT FROM AUTHOR]

Many travel surveys used in transportation planning still collect information over a single weekday while mobility and activity patterns fluctuate from day to day. However, the emergence of new continuous data sources has led to the generation of large amounts of longitudinal data. Therefore, this paper aims to value these two data types to cross-analyze the temporal variability of aggregated travel behaviors. A weighting method based on large-scale single-day survey samples was first developed to extend the representativeness of the daily trip diaries available in the three most recent Origin-Destination household travel surveys of Montreal, Canada, to the entire data collection period (four months in the fall). This method was then validated using passive data streams (smart card and count data) for three modes: car, subway, and bicycle. The variations in the daily use of these modes measured independently by the surveys and passive data were compared over several months of the fall period and throughout an average week. Indicators were also proposed to evaluate whether the two data sources capture the same trend and seasonality, especially the weekly rhythms. The results show that it is possible to accurately infer day-to-day variability in travel behavior from a large-scale cross-sectional single-day survey. Furthermore, this paper demonstrates the potential for complementarity between traditional surveys and emerging data (at least over the survey period), as well as the possibility to combine them since there are large similarities in the aggregated travel patterns they allow monitoring. [ABSTRACT FROM AUTHOR]

• Optimal protection against sea level rise can decrease transportation equity. • The model system incorporates hydrodynamic and transportation simulations. • A case study is based on the Communities of Concern in the SF Bay Area. • Accessibility and mobility of disadvantaged communities are studied. Transportation equity is an essential aspect of urban transportation planning. With climate change becoming inevitable, coastal cities are considering the mitigation of the impact of sea level rise on infrastructure. Transportation equity and sea level rise adaptation are usually considered separately. However, research pointed out that these two challenges could have considerable overlap and interaction. The present paper discusses transportation equity issues resulting from the impact of sea level rise and associated protection strategies. A case study of the San Francisco Bay Area points out cases where transportation equity can be negatively impacted when the optimal protection strategy against sea level rise is implemented. An integrated hydrodynamic and transportation model system is used in the present paper to demonstrate several scenarios where the most efficient protection strategies for the whole region increase the inequity that exists between the disadvantaged communities and other communities. Nevertheless, this impact can be mitigated with a relatively small addition to the protection strategy. The paper suggests that transportation equity cannot be overlooked in planning climate adaptation, as even the protection plan that maximizes benefits for the region may negatively impact the most vulnerable communities. [ABSTRACT FROM AUTHOR]

This paper mainly studies the joint decision of transportation mode and path in the multi-mode transportation network to provide the optimal plan for freights. This paper constructs a multi-mode transportation network system by setting virtual connections between networks with different transportation modes. The Dijkstra and multi-objective optimization algorithms are used to select the path in the network. After determining the optimal path, the paths' time, cost, and risk functions are established. The multi-objective function is converted into a single objective function by setting constraint conditions through the analytic hierarchy process. Then, the function is optimized by using the gradient descent method. Finally, the transportation plan for the case of chemical freights is formulated by using the above algorithms. The results show that the proposed algorithm can successfully find the solution for the joint decision of transportation mode and path in the complex network. After a quantitative analysis of the planned effect, the optimization actions of changing the initial transportation time and adjusting the upper limit of resources are proposed. The study findings provide a theoretical basis for improving the efficiency of the comprehensive transportation network. [ABSTRACT FROM AUTHOR]