Transportation Network Research Articles

To ride-hail or not to ride-hail? Complementarity and competition between public transit and transportation network companies through the lens of app data

AbstractTransportation Network Company (TNC) services have become a prominent factor in urban transportation in recent years, and there is an ongoing debate regarding their relationship with public transit. While many argue that TNCs draw passengers away from public transportation, others believe the two modes complement each other. However, due to the inadequate sample size of rider surveys as primary data sources, our understanding of how riders choose between these two modalities remains limited. This study uses nine months of trip planning data generated by the Transit App, which captures how travelers engage with multiple options in real time, including TNC and public transit services. We extract measures from Transit describing the travel options and the habits of each individual user for sessions in which the user “tapped” on one of these two modes, indicating consideration of it as an option. Machine learning models predict the likelihood of a rider tapping TNC based on features of the available public transit options and other contextual factors (e.g., time of day, weather conditions). The models find that these taps are driven by factors that highlight the convenience of TNC, such as the waiting time, walking distance, and the number of transfers for public transportation trips. We also find that the majority of TNC trips tapped by app users combine the two modes when using the Transit App, with TNC acting as a connection to or from public transit. These results provide detailed additional evidence for current arguments for both competition and complementarity between TNC and public transit from a population that uses an app to navigate public transit.

Mapping out the current and ideal weekly distribution of remote work in France after COVID-19

The recent rise of telework brings new debates on the impacts on travel behavior and transportation systems. One key issue concerns the weekly number and distribution of remote workdays, which affects transportation networks and their congestion levels. Using data from an online survey conducted in spring 2022 in France, Bayesian regressions and a multilevel model are used to analyze employee’s current and ideal weekly number and distribution of full-day home-based remote workdays. Firstly, the findings highlight a latent demand for more remote workdays, especially among people working remotely 1 day a week. Secondly, data show a concentration of current and ideal remote workdays on Fridays, followed by Mondays and Wednesdays. Thirdly, and finally, our results show that employer’s strategies and the nature of the sector (public or private) both contribute to explain the current and ideal weekly distribution of remote workdays. These results indicate that remote work should continue to develop and be distributed unevenly over the days of the week. This uneven distribution should impact travel behavior and traffic congestion.

Probabilistic seismic demand analysis for bridges based on earthquake scenarios

The current probabilistic seismic demand analysis of structures predominantly considers the correlation between the ground motion intensity measure (IM) and structural seismic demand, but fails to effectively capture regional source characteristics. It is essential to integrate earthquake engineering with seismic response of structures using source parameters to quantify regional ground motion uncertainties and determine appropriate IMs. This study presented a probabilistic seismic demand analysis method for structures based on earthquake scenarios, characterized by 1) utilizing the stochastic finite-fault method to simulate ground motions at a specific site instead of employing multiple seismic waves from different source areas, and 2) incorporating the correlation between IMs and the key indicator of source parameters, stress drop, into the probabilistic seismic demand analysis. Taking a continuous girder bridge as an example, the performance of the stress drop and 14 individual IMs in predicting the longitudinal seismic demand and reflecting source characteristics was evaluated. The results revealed that it was difficult to establish a reliable relationship between the stress drop and structural seismic demand. Owing to the complexities of ground motion and structural parameters, the individual IMs were challenging to effectively capture their characteristics simultaneously. Therefore, composite IMs have been proposed to cover the characteristics of both the source and seismic demand. The optimal solutions were derived using a multi-objective genetic algorithm, which exhibited desirable capabilities in both aspects, providing a comprehensive guide for the seismic performance assessment of regional transportation networks.

Can Transportation Networks Contribute to the Sustainable Development of Urban Agglomeration Spatial Structures?

The advancements in global urbanisation have significantly elevated the configuration of transportation networks for the sustainable development of urban agglomeration spatial structures (UASS). Using a comprehensive panel dataset covering 140 counties and districts in the Chengdu-Chongqing Economic Circle from 2014 to 2023, this study empirically examines the impact of transportation networks on the sustainability of UASS. It elucidates the underlying mechanisms using spatial Durbin, spatial spillover and multiple mediation effect models. The findings reveal that transportation networks considerably enhance the sustainability of UASS and generate substantial positive spatial spillover effects. The decay boundary of the spatial spillover effects exhibits a transition from positive to negative, eventually approaching ineffectiveness. The heterogeneity analysis indicates significant variations in terms of transportation network expansion and regional functional division on the sustainability of UASS across regions with various levels of economic development. This outcome suggests that regional development policies should carefully consider regional economic development disparities. The mechanism analysis further reveals that transportation networks enable the sustainability of UASS by accelerating regional hierarchical systems and regional connectivity.

End-to-end latency upper bounds and service chain deployment algorithm based on industrial internet network

The diverse service requests in industrial Internet networks require flexible and efficient service chain deployment to ensure the quality of service (QoS). However, current deployment algorithms for service chains are primarily designed to guarantee only low end-to-end latency; they often overlook the amount of service chains that can be accommodated by the network and could lead to severe network load imbalances, significantly reducing service efficiency and causing serious network congestion issues. To address the above issues, we develop a mathematical model of the network topology and service request chains by integrating Network Function Virtualization (NFV) and Software Defined Networking (SDN). Utilizing network calculus theory, we derive the upper bound of end-to-end delay for service chain routing and analyzed the relationship between the upper bound of service chain routing delay and the resource allocation of Virtual Network Function (VNF) nodes. Based on the aforementioned model, we propose a novel service chain deployment algorithm named the Delay-Aware Load-Balanced Routing Algorithm (DLBRA). DLBRA comprehensively considers network traffic load balancing and end-to-end latency of service chains, rationally allocating VNF node resources to complete the determined service chain routing deployment. Experimental results indicate that, compared to the shortest path and load balancing algorithms, DLBRA not only ensures that the end-to-end delay of the service chain meets its QoS requirements, but also effectively reduces network load imbalance, significantly increasing the number of service chain requests that the network can accommodate. Additionally, DLBRA provides tailored deployment guidance for different types of service chains, such as latency-sensitive and data-intensive service chains, ensuring optimal utilization of network resources. This algorithm enhances the efficiency of service chain deployment in industrial internet scenarios and possesses broad application potential in other network environments where delay optimization and load balancing are critical, such as intelligent transportation, cloud computing, and 5G networks.

A two-echelon multi-trip vehicle routing problem with synchronization for an integrated water- and land-based transportation system

This study focuses on two-echelon synchronized logistics problems in the context of integrated water- and land-based transportation (IWLT) systems. The aim is to meet the increasing demand in city logistics as a result of the growth in transport activities, including parcel delivery, food delivery, and waste collection. We propose two models, a novel mixed integer linear joint model, and a logic-based Benders’ decomposition (LBBD) model, for a two-echelon problem under realistic settings such as multi-trips, time windows, and synchronization at the satellites with no storage and limited resource capacities. The objective is to optimize transfers and satellite assignments, thereby reducing overall logistics costs for street vehicles and vessels. Computational experiments demonstrate that the LBBD model is more robust in terms of solution quality and solution time on average while the added value of the LBBD is more evident when solving large-scale instances with 100 customers, reducing the overall costs by 10.6% on average and significantly reducing the fleet costs on both networks. Furthermore, we assess the effect of changing cost parameters and satellite locations in the proposed IWLT system–analyzing system behavior and suggesting potential improvements–and evaluate several system alternatives in city logistics–consisting of different transportation network designs (single- and two-echelon), vehicle types, and operational constraints. On average, the proposed two-echelon IWLT system reduces the number of kilometers traveled by vehicles at street level by ranging from 20% to 30% compared to a typical single-echelon service design that relies solely on trucks.

Low-Carbon Water–Rail–Road Multimodal Routing Problem with Hard Time Windows for Time-Sensitive Goods Under Uncertainty: A Chance-Constrained Programming Approach

In this study, a low-carbon freight routing problem for time-sensitive goods is investigated in the context of water–rail–road multimodal transportation. To enhance the on-time transportation of time-sensitive goods, hard time windows are employed to regulate both pickup and delivery services at the start and end of their transportation. The uncertainty of both the demand for time-sensitive goods and the capacity of the transportation network are modeled using L-R triangular fuzzy numbers in the routing process to make the advanced routing more feasible in the actual transportation. Based on the carbon tax policy, a fuzzy linear optimization model is established to address the proposed problem, and an equivalent chance-constrained programming formulation is then obtained to make the solution to the problem attainable. A numerical experiment is carried out to verify the feasibility of incorporating the carbon tax policy, uncertainty, and water–rail–road multimodal transportation to optimize the low-carbon freight routing problem for time-sensitive goods. Furthermore, a multi-objective optimization is used to reveal that lowering the transportation costs, reducing the carbon emissions, and avoiding the risk are in conflict with each in the routing. We also analyze the sensitivity of the optimization results concerning the confidence level of the chance constraints and the uncertainty degree of the uncertain demand and capacity. Based on the numerical experiment, we draw several conclusions to help the shipper, receiver, and multimodal transportation operator to organize efficient water–rail–road multimodal transportation for time-sensitive goods.

Big geo-data unveils influencing factors on customer flow dynamics within urban commercial districts

Commercial districts, as the epicenters of urban commerce and economic activity, largely reflect an area’s prosperity through their customer flow. However, previous research, which often relied on statistical and survey data, has typically not captured the full scope of customer flow dynamics throughout urban commercial districts and has not adequately measured the specific impacts of business district locations and their surrounding communities on customer flow. To bridge these gaps, this study utilizes multidimensional big geo-data resources, including mobile phone signaling data, Points of Interest (POI) data, and transportation network data, to uncover the underlying factors that influence customer flow within urban commercial districts. The findings suggest that several factors—the size of the commercial district, the diversity of business formats, the convenience of parking, the working and residential population in surrounding communities, and the proximity to urban centers—significantly influence the customer flow. Consumers show a preference for larger-scale, centrally-located commercial districts that offer convenient parking options, while a homogenized and uncharacteristic business format may reduce a commercial district’s appeal. Furthermore, the study reveals that industrial parks and mixed-use complexes within the 15-minute living circle surrounding the commercial district have a stronger attraction to customer flow than residential neighborhoods do. The insights from this research not only guide the strategic placement of new commercial centers but also provide a robust framework for enhancing the layout of urban commercial spaces and for the revitalization and advancement of established commercial districts.

Optimal Configuration for Shared Electric-hydrogen Energy Storage for Multiple Integrated Energy Systems With Mobile Hydrogen Transportation

The flexible operation and storage of hydrogen and electric energy provide an effective path for the development of low-carbon energy and transportation systems. This paper introduces a configuration method for electric-hydrogen shared energy storage supporting the multiple energy and capacity demands of integrated energy systems (IESs). A Stackelberg game-based shared energy framework with gaseous hydrogen transportation by transportation network for shared energy storage operators (SESO) and IESs is established. In this framework, the electric power and electric storage sharing are accomplished by power lines, while the shared hydrogen is achieved by the transportation of mobile trucks. The target of the energy storage operator is to maximize its trading benefits with IESs and lower the life-cycle cost of the whole system, while the IESs aim to meet their energy demand and lower the operation cost via energy storage sharing. To solve this planning model, a two-level sine cosine based grey wolf optimizer (GWO-SCA)-bisectional method is provided to realize the system configuration, mobile hydrogen planning, and economic trading of the shared energy operator. A case study with 3 IESs, real-world geographic roads, and environmental conditions is carried out to verify the effectiveness of the method and the life-cycle configuration and operation economy of the shared energy storage. The comparisons show that the proposed energy storage sharing frame can achieve a higher energy utilization ratio of 92% and the proposed method can solve the two-level problem more efficiently, the calculation time is reduced by 80.2%.

A cross-sectional geospatial analysis of access to cesarean delivery in Uganda by facility functionality

Introduction: There is minimal data on spatial access to surgical maternal care in Uganda. We aimed to determine geospatial access to facilities performing cesarean delivery in Uganda at differing facility functional levels. Methods: We defined three levels of facility functionality: level 1 – functional by designation, i.e., health system level; level 2 – performed at least one cesarean delivery in the past year; and level 3 – performed at least 20 cesarean deliveries in the past year. We mapped facilities in ArcGIS Pro using GPS coordinates, calculated Manhattan distances, and using transportation network data, identified the area indicating 30-minute, 1-hour, and 2-hour geographic walking and driving access to facilities. The population estimate raster was overlain with drive time analysis areas to calculate the total population catchment within each travel time. Results: Of 6937 total facilities, 339 (4.9%) met level 1 functionality, 287 (4.1%) met level 2 functionality, and 241 (3.5%) met level 3 functionality. The percentages of women within 30-minute, 1-hour, and 2-hour walking times from level 1 facilities were 9.7%, 19.8%, and 34.8%, respectively. These percentages decreased to 9.2%, 19%, and 33% for level 2 functionality and 8.1%, 17.4%, and 30.5% for level 3 functionality, respectively. Access varied regionally, with the poorest access in the Northern region (20% within 2 hours walking) compared to the Central region (55.2% within 2 hours walking). Conclusion: We demonstrate a substantial proportion of the Ugandan population does not have timely access to cesarean delivery, with substantial subnational variation. This inequity in access to surgical obstetric care is vital information that can assist policymakers in achieving universal maternal health access.

CATOM : Causal Topology Map for Spatiotemporal Traffic Analysis with Granger Causality in Urban Areas.

The transportation network is an important element in an urban system that supports daily activities, enabling people to travel from one place to another. One of the key challenges is the network complexity, which is composed of many node pairs distributed over the area. This spatial characteristic results in the high dimensional network problem in understanding the 'cause' of problems such as traffic congestion. Recent studies have proposed visual analytics systems aimed at understanding these underlying causes. Despite these efforts, the analysis of such causes is limited to identified patterns. However, given the intricate distribution of roads and their mutual influence, new patterns continuously emerge across all roads within urban transportation. At this stage, a well-defined visual analytics system can be a good solution for transportation practitioners. In this paper, we propose a system, CATOM (Causal Topology Map), for the cause-effect analysis of traffic patterns based on Granger causality for extracting causal topology maps. CATOM discovers causal relationships between roads through the Granger causality test and quantifies these relationships through the causal density. During the design process, the system was developed to fully utilize spatial information with visualization techniques to overcome the previous problems in the literature. We also evaluate the usability of our approach by conducting a SUS(System Usability Scale) test and traffic cause analysis with the real-world data from two study sites in collaboration with domain experts.

Coal-derived electrically conductive asphalt pavements for snow/ice melting: From laboratory to field

Timely removal of ice and snow from roads is critical to safe, fast, and uninterrupted transportation networks in cold regions. Constructing electrically conductive asphalt pavements to melt the ice and snow on the roads through resistive heating is an emerging alternative technology to traditional snow/ice removal approaches such as utilizing snowplow machines and deicing chemicals. Carbon-based fibers and fillers including carbon fiber and graphite have been widely reported to make electrically conductive hot mix asphalt mixtures for pavement snow/ice-melting applications. This study aimed to develop and demonstrate a novel type of electrically conductive asphalt pavements for snow/ice-melting, which utilizes electrically conductive cold mix asphalt (CMA) mixtures incorporating coal-derived carbon-based coke aggregate as resistive heating elements. Both laboratory experiments and field tests were conducted to investigate the electrical, mechanical, and thermal properties of such electrically conductive asphalt mixtures and pavements. The laboratory experiment results indicated that the electrically conductive CMA mixtures incorporating coke aggregate had sufficiently high electrical conductivity and satisfactory mechanical performance and the pavement prototype slab utilizing a thin layer of such CMA mixtures could successfully raise the pavement surface temperatures to 8.3–11.7 °C from a low temperature of −5 °C with an input power density of 473 W/m2. The field test results showed that the full-scale coal-derived electrically conductive asphalt pavements were able to increase the pavement surface temperatures when electricity was applied, but the magnitude of temperature increase was highly dependent on the power density. Therefore, it is promising to use coke aggregate to construct coal-derived electrically conductive asphalt pavements for snow/ice melting.

Metaheuristic optimized complex-valued dilated recurrent neural network for attack detection in internet of vehicular communications

The Internet of Vehicles (IoV) is a specialized iteration of the Internet of Things (IoT) tailored to facilitate communication and connectivity among vehicles and their environment. It harnesses the power of advanced technologies such as cloud computing, wireless communication, and data analytics to seamlessly exchange real-time data among vehicles, road-side infrastructure, traffic management systems, and other entities. The primary objectives of this real-time data exchange include enhancing road safety, reducing traffic congestion, boosting traffic flow efficiency, and enriching the driving experience. Through the IoV, vehicles can share information about traffic conditions, weather forecasts, road hazards, and other relevant data, fostering smarter, safer, and more efficient transportation networks. Developing, implementing and maintaining sophisticated techniques for detecting attacks present significant challenges and costs, which might limit their deployment, especially in smaller settings or those with constrained resources. To overcome these drawbacks, this article outlines developing an innovative attack detection model for the IoV using advanced deep learning techniques. The model aims to enhance security in vehicular networks by efficiently identifying attacks. Initially, data is collected from online databases and subjected to an optimal feature extraction process. During this phase, the Enhanced Exploitation in Hybrid Leader-based Optimization (EEHLO) method is employed to select the optimal features. These features are utilized by a Complex-Valued Dilated Recurrent Neural Network (CV-DRNN) to detect attacks within vehicle networks accurately. The performance of this novel attack detection model is rigorously evaluated and compared with that of traditional models using a variety of metrics.

Transportation connectivity development strategy in the Wakatobi islands, Indonesia

This study aims to analyze connectivity or accessibility between regions in Wakatobi islands, both within and between islands, to understand the available transportation network. Based on an understanding of the dynamics of connectivity, it is expected to provide a solid foundation for the development of more efficient and sustainable transportation infrastructure in the future. A combination of qualitative and quantitative approaches is used to explore data more comprehensively and accurately. The two primary airports and several ports are still insufficient in enhancing connectivity for both the residents and tourists within the archipelago. Improving road, sea, and air transportation networks is a necessity and expectation to improve connectivity between regions. An analysis of accessibility potential provides an overview of transportation costs and expensive and long travel fares. There are several needs that need to be met in the form of the revitalization of local ports, the development of the concept of Air Buses between crossing ports, optimizing routes between airports, and the implementation of Bus/BRT (Bus Rapid Transit) on each island with feeder lines. Furthermore, the development of connectivity in Wakatobi must consider various alternative modes of transportation, increasing service frequencies, and developing supporting infrastructure. This conclusion is the basis for the preparation of a holistic and sustainable connectivity development plan in the Wakatobi archipelago.

Risk assessment of urban infrastructure vulnerability to meteorological disasters: A case study of Dongguan, China

Effective forecasting and response to meteorological hazards are crucial for safeguarding life, property, and supporting sustainable socioeconomic development. With the rising frequency and severity of meteorological hazards worldwide, this study proposes an enhanced risk assessment framework for urban infrastructure exposed to extreme weather events, with a focus on cascading impacts to critical services such as electricity, communication, and transportation networks (roads and subways). A disaster-loss model is developed to quantify infrastructure vulnerability at various spatial and temporal scales under heavy rainfall conditions, accounting for secondary effects. The model's performance is validated through empirical analysis of a 15-year rainfall event in Dongguan City, China, occurring from September 7–8, 2023. Results indicate the model's ability to predict real-event outcomes with approximately 70% accuracy. This model offers valuable insights for disaster prevention and mitigation strategies, aiding decision-makers in optimizing emergency resource allocation, enhancing disaster response efficiency, and issuing timely public risk warnings to minimize losses.

Exploring the critical factors in pharmaceutical supply chains revealed during COVID-19 and addressing missing links

The COVID-19 pandemic revealed profound weaknesses in global pharmaceutical supply chains, resulting in widespread shortages of critical medical supplies, delayed drug manufacturing, and disruptions in vaccine distribution. These supply chain vulnerabilities were exacerbated by several key factors, including an overreliance on a limited number of countries for active pharmaceutical ingredients (APIs), insufficient local manufacturing capacity, and logistical constraints in transportation and distribution networks. The pandemic also exposed inefficiencies in regulatory frameworks, leading to delays in approvals and limited agility in responding to rapidly changing demands. Furthermore, the lack of transparency and real-time monitoring across global supply chains hindered effective decision-making and coordination, aggravating the crisis. This paper provides an in-depth analysis of the critical factors that contributed to pharmaceutical supply chain disruptions during COVID-19, identifying missing links such as the need for diversification of supply sources, enhanced flexibility in manufacturing processes, and the integration of advanced technologies like artificial intelligence (AI), blockchain, and automation for supply chain optimization. The analysis highlights how supply chain visibility, digital innovation, and risk management strategies could improve resilience in the face of future global health crises. In addition to identifying these challenges, the paper proposes solutions to address the missing links in pharmaceutical supply chains, including the decentralization of production, the development of regional manufacturing hubs, and the creation of public-private partnerships to bolster local production. The role of government policies in facilitating faster regulatory approvals, fostering international cooperation, and incentivizing innovation is also explored. Case studies on successful post-COVID-19 adaptations, such as the rapid scale-up of vaccine production and the reconfiguration of personal protective equipment (PPE) supply chains, offer insights into effective strategies for building more resilient systems. Ultimately, this paper underscores the importance of a collaborative, multi-stakeholder approach to ensuring the stability and security of pharmaceutical supply chains. It calls for proactive measures to strengthen global supply chain infrastructures, improve regulatory agility, and embrace emerging technologies that can predict and respond to disruptions. These steps are crucial to safeguarding the continuous delivery of essential pharmaceuticals during future pandemics or other large-scale disruptions, ensuring global health security in an increasingly interconnected world.

Mutual Causality Between Urban Transport Superiority Degree and Urban Land Use Efficiency: Insights from County Cities in Gansu Province Under the Belt and Road Initiative

Exploring the coupled coordination and interaction between urban transport superiority degree (UTSD) and urban land use efficiency (ULUE) is the key to promoting efficient land use in cities and coordinated development. This paper adopts the improved UTSD model, super-efficiency slack-based measure–undesirable output model, coupling coordination degree model (CCDM), panel Granger causality test, random forest model, and the mixed geographically and temporally weighted regression model to reveal the spatial and temporal evolution and coupling characteristics of UTSD and ULUE in Gansu from 2005 to 2020 and to validate and explore the interaction mechanism between UTSD and ULUE. The results show that (1), from 2005 to 2020, the average UTSD in Gansu increased from 0.56 to 1.01 and the Belt and Road Initiative accelerated the construction of the transportation network in Gansu. The average ULUE increased from 0.52 to 0.62; the spatial distribution of ULUE was high in the west and north and low in the east and south. (2) From 2005 to 2020, the average CCDM of UTSD and ULUE in Gansu increased from slightly unbalanced (0.37) to slightly balanced (0.52). A spatially high UTSD and high ULUE agglomeration area can be found along the transportation arteries. (3) The UTSD and ULUE were mutually causal, with the degree of transportation arterial influence degree being the strongest driver of ULUE among the components of UTSD (30.41% contribution) and tax revenue being the strongest driver of UTSD among the components of ULUE (15.10% contribution). Overall, the connotation of ULUE puts forward the demand for improving the transportation infrastructure and, at the same time, provides the guarantee for UTSD upgrading, which in turn affects the ULUE. In the future, the Xinan region of Gansu should prioritize planning and construction of a transportation network. The results of this study can provide a scientific basis for the construction of transportation networks and the efficient use of urban land in Gansu and other regions.

Applicability of HEC-RAS and Geospatial Tools for Inland Waterways Transportation Corridor: Case Study of Ganga Basin, Bihar, India

Bihar has the largest networks of Rivers and drainage systems and most of them are perineal. Previously, north Bihar had active inland waterways transportation networks but these days the networks are not in use. Due to rapid urbanization and a hike in fuel prices, the transportation cost within the state has risen in the past few years. The demand for cheaper transportation systems is highly needed in these areas for goods transportation. Recently, the inland waterways authority of India has started a new shipping service in River Ganga only but a few more river networks can also be used for these kinds of activities. A study was conducted to find the inland waterways' potential for the development of a state river transportation corridor for North Bihar India. Various mathematical modeling tools such as HEC-RAS, HEC-HMS along with geospatial tools have been used for this study. The result obtained by the study indicates that the six more rivers of the states have the potential for inland waterways transportation along with suitable vessels for transportation. The methodology developed in the study is suitable for the development of waterways transportation corridors at various places. The study also emphasizes selecting the proper places for making the jetties which may be helpful for various activities such as disaster management, industrial fright services, logistic supply, etc.

Cybersecurity and defense in intelligent transportation systems

Intelligent Transportation Systems (ITS) have become increasingly crucial for enhancing transportation networks' efficiency, safety, and security. ITS includes advanced technologies such as electronic sensors, data transmission, and intelligent control technologies, providing better driver and rider services. However, these technologies pose significant security and privacy risks, in which attackers can easily disrupt transportation systems. This paper presents a survey of the challenges with ITS, the engineering requirements, attack challenges, and related work of securing ITS. This survey also discussed the elements of ITS, including advanced traffic management systems, connected and automated vehicles, intelligent infrastructure, and integrated data analytics, as well as the role of government policies, public-private partnerships, and stakeholder engagement. The aim is to look at present security challenges associated with ITS and provide better ways to the system. which is critical for creating smart cities and increasing the importance of individual and public transportation systems.

Transportation Network Density, Domestic Market Integration and Excess Sensitivity of Household Consumption

Abstract From the perspective of domestic market integration, this paper systematically examines the impact of transportation infrastructure conditions on excess sensitivity of household consumption based on the China Family Panel Survey (CFPS) and multi-level matching panel data of transportation network density. The results show that the fast-growing development of the transportation infrastructure network has a significant alleviating effect on excess sensitivity of household consumption along the route, and the conclusion is still robust after the use of the multi-dimensional instrumental variable method and a series of robustness tests. According to the heterogeneity tests, in terms of the alleviating effect of transportation infrastructure, railroads rank the first, highways the second, substandard roads the third, waterways the fourth, and roads of other grades at the bottom. The mechanism test reveals that the improvement of domestic market integration is an important channel for transportation infrastructure to alleviate excess sensitivity of household consumption. This paper confirms that improving the transportation infrustructure system is conducive to the construction of a unified national market, alleviating excess sensitivity of consumption and stimulating consumption.This paper provides suggestions for implementing the strategy of boosting domestic demand, and helps the government understand households’ consumption decision-making from a broader perspective. This study also provides a theoretical basis for the economic spillover effect of transportation infrastructure.