Transportation Network Research Articles

Nucleation phenomena and extreme vulnerability of spatial k-core systems

K-core percolation is a fundamental dynamical process in complex networks with applications that span numerous real-world systems. Earlier studies focus primarily on random networks without spatial constraints and reveal intriguing mixed-order transitions. However, real-world systems, ranging from transportation and communication networks to complex brain networks, are not random but are spatially embedded. Here, we study k-core percolation on two-dimensional spatially embedded networks and show that, in contrast to regular percolation, the length of connections can control the transition type, leading to four different types of phase transitions associated with interesting phenomena and a rich phase diagram. A key finding is the existence of a metastable phase where microscopic localized damage, independent of system size, can cause a macroscopic phase transition, a result which cannot be achieved in traditional percolation. In this case, local failures spontaneously propagate the damage radially until the system collapses, a phenomenon analogous to the nucleation process.

Travel Characteristics and Vulnerability Analysis of Road Resource Utilization Based on Taxi GPS Data

Residents’ travel and logistics are greatly affected by urban transportation networks, which are one of the most important supports for urban socio-economic activities. Urban transportation systems tend to cripple when faced with challenges such as natural hazards and social unrest. This paper proposes a framework for a vulnerability analysis of urban road networks (URNs) based on real traffic flows with GPS data. An improved K-shell critical node identification method is proposed based on structural and traffic characteristics. Then, a cascade failure model is proposed to analyze the structural and functional vulnerability of the URN by combining the load capacity model and the vulnerability model. This paper takes the Harbin main city URN as an example and first analyzes the passenger travel distribution and the relationship between travel orders, population and POI. Four deliberate attack methods are proposed to analyze the vulnerability of the URN under deliberate attack on commute days and rest days. The experimental results show that URNs exhibit intense vulnerability, with the fastest cascading failure occurring based on improved K-shell node failure. Furthermore, URNs are more vulnerable on rest days compared to commuter days. These findings could be used to inform a vulnerability-based spatiotemporal design of UBNs and provide theoretical support for managing traffic congestion on different days.

Process of Acquisition to Destination – A Bang on Economic Prudence with Reference to Indian Logistics Industry

The study explores the significance of the logistics industry in the Indian economy and examines its sector-wise impact on economic well-being. It emphasizes how the presence or absence of transport infrastructure significantly influences economic growth and global competitiveness. With globalization expanding its scope, the study addresses the challenges of global logistics, such as trade laws, cultural differences, and the necessity for efficient cross-border transportation networks. Moreover, the research delves into factors driving the growth of the Indian logistics industry. Innovations and developments are seen as pivotal to economic progress, with intelligent logistics and smart transportation playing crucial roles. The Logistics Performance Index (LPI), a benchmarking tool by the World Bank, ranks countries based on logistics performance. In the 2023 report, India achieved an LPI rating of 3.4, securing the 38th position among 139 countries. The study substantiates the factors fueling the logistics industry with statistical evidence, highlighting technological advancements and the integration of IoT-enabled facilities as key drivers. Furthermore, the study discusses strategic initiatives aimed at enhancing India's logistics infrastructure. The PM Gati Shakti initiative seeks to streamline infrastructure development by promoting integrated planning and coordinated implementation across government departments. Concurrently, the National Logistics Policy (NLP) provides a comprehensive framework for the industry's development. Additionally, the Unified Logistics Interface Platform (ULIP) facilitates real-time information exchange among stakeholders, thereby simplifying logistics planning.

The k-th order mean-deviation model for route choice under uncertainty

This study introduces the k-th order mean-deviation model for optimizing route choice within large, stochastic, and time-variant networks. This model addresses the limitations of the traditional mean-standard deviation approach by better handling extreme outcomes in travel times. It features an objective function called the “travel time budget”, which combines the average path travel time with a safety margin. This margin is defined by a trade-off coefficient and a selected deviation measure (total or semi) of the travel time. The model is divided into three variations: 1) The mean-total deviation (MTD) model for symmetric travel time distributions, 2) The mean-upper-semi-deviation (MUSD) model for asymmetric distributions prioritizing upper semi-deviations, suitable for risk-averse travelers, and 3) The mean-lower-semi-deviation (MLSD) model for asymmetric distributions focusing on lower semi-deviations, preferred by risk-prone individuals. We explore these models’ alignment with the stochastic dominance (SD) rule and develop a solution methodology based on SD principles. Numerical experiments in two real-world transportation networks demonstrate the models’ effectiveness and show how the choice of deviation affects route selection decisions.

Geographical analysis of the impact of road networks on traffic accidents in Pishdar District

An increase in the population and its needs, the development of transportation means and the diversification of transportation networks, as well as the differences in the geographical location of the population, are reasons for paying more attention to the road and transportation sector. In addition to all this, the increase in traffic accidents at the local and global levels is another reason for the attention of different fields. Pishdar district, as a geographical unit in the Kurdistan Region, has seen significant progress in the field of transportation in recent years, which has caused it to have its share of traffic accidents. The main goal of this study is to identify the characteristics of road networks in Pishdar district as well as a geographical analysis of traffic accidents and determine the level of solving these problems that are observed in the district in a scientific way. The research plan is divided into three main parts, each of which consists of two main sections: Part 1. This part describes the characteristics of transport networks and consists of two main sections. The first section includes the classification of road networks, and the second section includes the distribution of road networks at the level of administrative units in the study area. Part 2. This part is dedicated to the role of geographical principles in the time and place of traffic accidents in Pishdar district. The first is about the difference in time according to (month and year), and the second is the difference in location according to (city and outside the city). Part 3. This part is dedicated to how the most significant natural factors, which also include the most significant human factors, affect traffic accidents.

A methodology for ranking of critical links in transportation networks based on criticality score distributions

Criticality of transportation networks links is calculated through utilizing criticality metric(s) for various disruption scenarios that generally include one link removal/degradation at a time or simultaneous removal/degradation of multiple links. As also shown in this paper, single link removals do not reveal the actual criticality of link(s) due to network dependencies. Multiple simultaneous link removal scenarios can capture the network interactions; however, the results relate to the criticality ranking of scenarios than the links, e.g., the links in scenario-1 are more critical than the links in scenario-2. This paper addressed the need for a methodology that provides individual link criticality rankings based on failure scenarios. The proposed approach utilizes the distribution of the criticality scores for each link based on running traffic assignment for each failure scenario, e.g., the distribution for link #X is composed of the criticality scores of all link failure combinations that include link #X. The criticality ranking for each link is identified based on its criticality distribution's mean, coefficient of variation and skewness. The use of the proposed approach is illustrated on test networks to show that it provides robust link criticality rankings that accounts for network interactions across varying scales of link failure scenarios.

Dynamic Spatio-Temporal Adaptive Graph Convolutional Recurrent Networks for Vacant Parking Space Prediction

The prediction of vacant parking spaces (VPSs) can reduce the time drivers spend searching for parking, thus alleviating traffic congestion. However, previous studies have mostly focused on modeling the temporal features of VPSs using historical data, neglecting the complex and extensive spatial characteristics of different parking lots within the transportation network. This is mainly due to the lack of direct physical connections between parking lots, making it challenging to quantify the spatio-temporal features among them. To address this issue, we propose a dynamic spatio-temporal adaptive graph convolutional recursive network (DSTAGCRN) for VPS prediction. Specifically, DSTAGCRN divides VPS data into seasonal and periodic trend components and combines daily and weekly information with node embeddings using the dynamic parameter-learning module (DPLM) to generate dynamic graphs. Then, by integrating gated recurrent units (GRUs) with the parameter-learning graph convolutional recursive module (PLGCRM) of DPLM, we infer the spatio-temporal dependencies for each time step. Furthermore, we introduce a multihead attention mechanism to effectively capture and fuse the spatio-temporal dependencies and dynamic changes in the VPS data, thereby enhancing the prediction performance. Finally, we evaluate the proposed DSTAGCRN on three real parking datasets. Extensive experiments and analyses demonstrate that the DSTAGCRN model proposed in this study not only improves the prediction accuracy but can also better extract the dynamic spatio-temporal characteristics of available parking space data in multiple parking lots.

Spatial transformation of industrial territories: Experience and prospects of Tyumen

Transformation of the architectural heritage of large cities, which also includes that of industrial areas, can be seen as a development and reconstruction driver of modern urban development. In this work, we aimed to propose methods for spatial transformation of urban industrial or mixed areas in the structure of a large city. A structural assessment of the urban planning practice and experience in the city of Tyumen was conducted to identify architectural and planning approaches to the reconstruction of depressive post-industrial areas. Following an analysis of the existing location and concentration of industrial areas, along with their main parameters, and the experience of experimental design of renovated districts, the Babarinka industrial district in the northern part of the city was selected. This district is characterized by its direct interconnection with the city center, a combined type and small-cell structure of the existing buildings, and its historical interrelation with the open space of the river. Typological and morphological features of the existing industrial areas in the city, interacting with the surrounding urban areas in different ways, were determined based on the theory of mixed urban development. A combined assessment of urban planning data enables the key parameters of urban transformation to be determined and the basic planning structures to be identified. The latter include spatial planning, transportation and pedestrian networks, landscaping, and functional zoning. These planning structures determine the key qualitative parameters of the transformation design concept of industrial areas, thus serving as criteria for evaluating various-scale design solutions.

Solving Dynamic Full-Truckload Vehicle Routing Problem Using an Agent-Based Approach

In today’s complex and dynamic transportation networks, increasing energy costs and adverse environmental impacts necessitate the efficient transport of goods or raw materials across a network to minimize all related costs through vehicle assignment and routing decisions. Vehicle routing problems under dynamic and stochastic conditions are known to be very challenging in both mathematical modeling and computational complexity. In this study, a special variant of the full-truckload vehicle assignment and routing problem was investigated. First, a detailed analysis of the processes in a liquid transportation logistics firm with a large fleet of tanker trucks was conducted. Then, a new original problem with distinctive features compared with similar studies in the literature was formulated, including pickup/delivery time windows, nodes with different functions (pickup/delivery, washing facilities, and parking), a heterogeneous truck fleet, multiple trips per truck, multiple trailer types, multiple freight types, and setup times between changing freight types. This dynamic optimization problem was solved using an intelligent multi-agent model with agent designs that run on vehicle assignment and routing algorithms. To assess the performance of the proposed approach under varying environmental conditions (e.g., congestion factors and the ratio of orders with multiple trips) and different algorithmic parameter levels (e.g., the latest response time to orders and activating the interchange of trip assignments between vehicles), a detailed scenario analysis was conducted based on a set of designed simulation experiments. The simulation results indicate that the proposed dynamic approach is capable of providing good and efficient solutions in response to dynamic conditions. Furthermore, using longer latest response times and activating the interchange mechanism have significant positive impacts on the relevant costs, profitability, ratios of loaded trips over the total distance traveled, and the acceptance ratios of customer orders.

Control system for automatic adjustment of departure intervals of public transport vehicles

In numerous urban areas, fine-tuning bus schedules to align with fluctuating passenger numbers poses a sizable challenge, compounded by diverse factors such as weather and unexpected events. This paper conducts a comprehensive examination of existing optimization methods aimed at enhancing the departure intervals of public transport systems, particularly focusing on the Bidirectional Coordination Optimization Algorithm (BCOA) and the genetic algorithm. Through systematic analysis, these methodologies are meticulously explored for their effectiveness in addressing the scheduling challenges faced by urban transit systems. Additionally, it delves into specific instances where the Proportional-Integral-Derivative (PID) control mechanism has been employed to adeptly adjust the timing of bus departures based on real-time demand. The PID system's proven reliability and efficacy spotlight its potential as a transformative tool for advancing public transit systems. By leveraging such technology, there's tangible optimism for achieving a more responsive, efficient, and passenger-friendly public transportation network in the years to come.

A fairness-based multi-objective distribution and restoration model for enhanced resilience of supply chain transportation networks

Disruptive events play a significant role in the performance of supply chain networks (SCNs) due to their high dependency on transportation networks. Therefore, it is critical to develop coordinated strategies that consider (i) efficient restoration plans for the transportation network (TN) and (ii) distribution plans for downstream SCN, which consider critical aspects such as road congestion, travel time, and required service levels. Thus, we propose a novel multi-objective non-linear mixed-integer programming (NMIP) model to deal with disruptions in transportation networks while maximizing SCN performance. We developed a model for optimal restoration that reduces the effects of disruptions that disrupt the performance of the road TN and SCNs while considering a fair distribution of commodities. The model provides optimal restoration strategies for the TN after unpredicted events, not only based on a cost-effectiveness approach but also on the fair distribution of commodities among demand nodes on a complex network structure with a multi-supplier, multi-demand, and multi-commodity framework. To illustrate the model's capabilities, we study the transportation of commodities in Colombia and how disruptions impact their SCN performance. The results show that a fairness-based distribution helps to obtain satisfaction rates faster when compared to restoration plans based on cost-effectiveness only.

Investigating urban mobility through multi-source public transportation data: A multiplex network perspective

The integration of multi-source and diverse spatio-temporal travel data provides a comprehensive insight into urban mobility. Using data from Shenzhen's public transportation system, this study presents an analytical framework based on multiplex networks to examine variations in multi-mode public transportation usage (metro, bus, taxi, and shared bike) and their correlation with the built environment. This framework encompasses the analysis of network topological characteristics, centrality, and communities. The examination of network topological characteristics reveals that the multiplex transportation network exhibits high global accessibility and local connectivity. Network centrality analysis, focusing on weighted outdegree centrality, captures the patterns of public transportation ridership. Centrality modeling, employing the light gradient boosting machine, demonstrates a nonlinear relationship between ridership and the built environment. Factors including population density, residential land use percentage, entertainment service density, restaurant density, and metro station density consistently exhibit positive correlations with ridership across different times of the day. The community structure analysis, using consensus community detection, indicates that distinct urban areas exhibit clustering behavior based on public transportation demand patterns, forming distinct communities that closely align with the functional zoning of urban planning. These findings could provide valuable insights for the strategic planning of transportation services and the built environment.

Impact of Transportation Network Companies on Conventional Taxi Companies in Indonesia

Impact of Transportation Network Companies on Conventional Taxi Companies in Indonesia

Surface Subsidence Characteristics and Causes Analysis in Ningbo Plain by Sentinel-1A TS-InSAR

In recent years, the Ningbo Plain has experienced significant surface subsidence due to urbanization and industrialization, combined with the area’s unique geological and hydrological conditions. To study the surface subsidence and its causes in the Ningbo Plain, this study analyzed 166 scenes of Sentinel-1A SAR images between January 2018 and June 2023. The time series interferometric synthetic aperture radar (TS-InSAR) technique was used to acquire surface subsidence information in the area. The causes of subsidence were analyzed. The results show that: (1) the annual deformation rate of the Ningbo Plain ranges from −44 mm/yr to 12 mm/yr between 2018 and 2023. A total of 15 major subsidence zones were identified by using both the subsidence rate map and optical imagery. The most severe subsidence occurred in the northern industrial park of Cixi City, with a maximum subsidence rate of −37 mm/yr. The study reveals that the subsidence issue in the main urban area has been significantly improved compared to the 2017 subsidence data from the Ningbo Bureau of Natural Resources and Planning. However, three new subsidence areas have emerged in the main urban area, located, respectively, in Gaoqiao Town, Lishe Town, and Qiuyi Village, with maximum rates of −29 mm/year, −24 mm/year, and −23 mm/year, respectively. (2) The causes of subsidence were analyzed using various data, including land use data, geological data, groundwater-monitoring data, and transportation network data. It is found that a strong link exists between changes in groundwater levels, compressible layer thickness, and surface subsidence. The groundwater levels changes and the soft soil layer thickness are the main natural factors causing subsidence in the Ningbo Plain. Additionally, the interaction between static loads from large-scale industrial production and urban construction, along with the dynamic loads from transportation networks, contribute significantly to surface subsidence in the Ningbo Plain. The results from this study enhance the understanding of the driving factors of subsidence in the Ningbo Plain, which can provide necessary guidance for the economic development and decision-making in the region, helping to manage and potentially mitigate future subsidence issues.

Comparative analysis of the optimal route recommendation models based on city public transportation network data

In recent years, studies on smart cities have attracted much attention in the literature. In the smart city concept, urban public transportation network studies are one of the most important issues. Afterthe 2000s, smart cards have been widely used in city public transportation systems. Using the data collected through the smart card, many models have been created in line with the smart city concept. In the literature, there are various optimal route recommendation models using urban public transportation network and city smart card data. In this study, some current models built on the basis of urban public transportation network and smart card data are discussed. The specifications of the models, their important differences, the topologies they use, optimization criteria, and computational complexities are analyzed. Dijkstra’s algorithm, which is widely used for the solution of optimal route recommendation models, and its various modifications are analysed. Additionally, various models developed by applying fuzzy logic are examined. Comparative analysis of public transportation network models is given.

Correlation analysis of InSAR displacement data and inspection data towards multiple bridge monitoring in transportation network

This paper presents a study on multiple bridge monitoring in a wide area of transportation networks using satellite Synthetic Aperture Radar (SAR) technology. SAR has been expected to have potential to analyze displacements of multiple large-scale structures in wide areas and in long-term without installing any sensors on them. This study shows quantitative analysis of InSAR (interferometric SAR) data on multiple bridges regarding to structural condition using their periodic inspection data. The target bridges are seventeen bridges located at a region in Japan, Ibaraki prefecture. They are the bridges that has lengths over 100 m selected from ones operated by the rural-road office of MLIT, the transport ministry of Japan, where periodic inspection data are all opened and available. Their displacements are then obtained using Sentile1 satellite SAR data. The time period for the analysis is seven years from 2016 to 2022, with time interval of 12 days. Using those data, line-of-sight (LoS) displacements of each bridge are derived as a time-series data through interferometric processing. The LoS displacement time-series data are then analyzed for each bridge by principal component analysis (PCA) to extract significant components of time-series deformation behavior. The extracted principle components, air temperature, and inspection records are compared and analyzed. As a result, it is found that a stable correlation between the principle component and air temperature could be one of explanatory factors of bridge conditions. This may indicate the bridges having stable correlation between LoS displacements and air temperature are in a nominal and undamaged condition although further investigation to more bridges is required. Moreover, visualizations of time-series InSAR bridge deflection and extracted feature components and its pattern analysis in multiple bridges are shown to have high potential to contribute on multiple bridge operations in whole transportation network.

Battery Charging and Swapping System Involved in Demand Response for Joint Power and Transportation Networks

Battery Charging and Swapping System Involved in Demand Response for Joint Power and Transportation Networks

Spatial Pathology of Earthquake Management in Salas Babajani County, Kermanshah, Iran

Background and objective Human settlements are formed and developed in a specific natural environment. Social, political, cultural, and economic systems that govern the societies can affect residential spaces. Therefore, the special natural and human habitats cause the formation of special spaces, which require their own management system. After the earthquake on November 12, 2017, in Salas Babajani County, Kermanshah, Iran, whose epicenter was Ezgeleh district, the presence of natural areas in the county caused the crisis management in this region to face special problems. In addition, the sectoral and centralized management structure governing the country and thus the Salas Babajani County added to the complexity of these problems and caused the residents of this region to suffer a deep crisis. The aim of the current study is a spatial pathology of earthquake management in urban and rural settlements of Salas Babajani County. Method This is a descriptive-analytical study. Data were collected using the documentary method and a researcher-made questionnaire. Participants were the residents of Salas Babajani County. Results The findings showed that the earthquake management in the County has a high compliance with the centralized and sectoral management model. In addition, it was found that the crisis management in the four stages (prevention, preparedness, response, and recovery) was in an unfavorable situation. the highest weakness was in the prevention stage and the lowest weakness was related to the preparedness stage. Conclusion Overall, the results showed that crisis management has worsened by moving away from the center towards the outskirts, especially towards the southwestern borders. In other words, by moving away from the political center of the County (Tazeh Abad district), the crisis management became very unfavorable. Also, it was found that the crisis management was greatly influenced by the spatial factors of elevation, land slope, transportation network, distribution of human settlements, and population density.

Transit fares integrating alternative modes as a delay insurance

Public transport (PT) fare policy remains subject to innovations, not least evident in the Mobility as a Service discussion. Mode integration and related fare strategies can be used to increase the attractiveness of PT by compensating for potential delays. This study proposes “premium fares” as a novel pricing tool that can evaluate and improve the travel time reliability on a multimodal transportation network. The premium fare is higher than the standard fare but allows passengers to use an alternative service free of charge if waiting for the delayed public transport service is anticipated to be longer than a certain qualification threshold. Properties that guarantee monotonicity of the premium fare with respect to distance traveled are developed. The operator aims to find the premium fare price and qualification threshold that can maximize its profit, based on the probability distributions of delay and passengers’ value of time (VOT). We model this optimization problem for a railway line given a limited capacity of alternative mode services, e.g., the number of taxis. A two-stage approach using nonlinear and dynamic programming is developed to obtain the optimal decision variables and associated capacity allocation plan. Our results show that the introduction of the premium fare can benefit both operators and travelers with increased profits and improved travel time reliability. The interplay between fares and the qualification threshold is illustrated using various delay and VOT distributions. To attract enough customers the premium fare has to be set below a specific level dependent on the VOT distribution. Meanwhile, the operator adjusts the qualification threshold to control the cost paid to the alternative service provider.

Strengthening the transportation network to improve diagnosis of multidrug-resistant tuberculosis: role of postal services

Tuberculosis (TB) has been acknowledged as one of the major public health concerns, being present in all the nations and has the potential to affect people from all age groups. Ensuring prompt diagnosis of TB is an essential step to improve the prognosis of the patients and minimize the development of complications. To accomplish the set targets under the end TB strategy, the diagnostic arm in TB control activities needs to be immensely strengthened and expanded. We have to ensure that the quality of the sample remains the same during the transport, especially in those regions where reference laboratories are present in distant locations. In conclusion, in the battle against TB, there is an immense need to strengthen the diagnostic services to enable early detection and prompt initiation of treatment. However, the same becomes a major challenge in resource-constrained settings, especially for making a diagnosis of multidrug-resistant-TB. This calls for the need to explore alternative options to expedite the process and roping in the postal network have emerged as a feasible and effective mechanism to overcome the existing challenges.