Transportation Network Performance Research Articles

A Max–Min Fairness-Inspired Approach to Enhance the Performance of Multimodal Transportation Networks

Disruptions in multimodal transportation networks can lead to significant damage and loss, affecting not only the networks’ efficiency but also their sustainability. Given the size, dynamics, and complex nature of these networks, it is essential to understand and enhance their resilience against disruptions. This not only ensures their functionality and performance but also supports sustainable development by maintaining equitable service across various communities and economic sectors. Therefore, developing efficient techniques to increase the robustness and resilience of transportation networks is crucial for both operational success and sustainability. This research introduces a multicriteria mixed integer linear programming (MCMILP) model aimed at enhancing the resilience and performance of multimodal–multi-commodity transportation networks. By ensuring effective distribution of commodities, alongside a cost-efficient distribution strategy in the wake of disruptive events, our model contributes significantly to sustainable transportation practices. The proposed MCMILP model demonstrates that integrating equality considerations while seeking a cost-efficient distribution strategy significantly mitigates the impact of disruptions, thereby bolstering the resilience of multimodal transportation networks. To illustrate the capabilities of the proposed modeling approach, we present a case study based on the multimodal transportation network in Colombia. The results show a significant improvement in the number of nodes that satisfy their demand requirements with respect to other approaches based on reducing total unsatisfied demand and transportation costs.

The application of CNN in Smart city management and supervision under the background of Smart city

Intelligent transportation management, as an important component of intelligent city supervision, has practical value in improving the effectiveness of urban management. To improve the efficiency of intelligent city traffic management, this study proposes to use convolutional neural networks to improve the performance of navigation and intelligent transportation networks in intelligent transportation. This network is used for vehicle recognition in navigation to predict road congestion and determine the optimal driving path. This structure is applied in the intelligent road network to monitor the driving status of vehicles on the road. It is combined with the vehicle networking system to locate abnormal vehicles to achieve intelligent traffic management and supervision. These results confirm that this algorithm achieves a vehicle recognition accuracy of 91.2 % after 96 iterations and completes the recognition process within 163 ms. This algorithm converges to a vehicle positioning accuracy of 82.4 % after 93 iterations and achieves positioning within 277 ms. This algorithm can not only improve the optimal path planning effect of navigation, but also identify abnormal vehicles within 77 ms. Therefore, the proposed algorithm has high practical value in intelligent transportation management.

An explicit approach for modeling the performance of transportation networks immediately after an earthquake

Surface transportation systems play a vital role in supporting a region’s functionalities. They are expected to remain operational before and even after a hazardous event (e.g. an earthquake). The importance is evident to estimate the post-disaster performance of traffic systems under a probability-based framework, considering the uncertainties arising from both the hazards and the transportation infrastructure fragilities. This paper proposes an explicit approach for evaluating the performance of transportation systems immediately after an earthquake event. The method estimates the spatial distribution of vehicles in the traffic network in a closed form and thus is relatively efficient compared with traditional methods (e.g. an agent-based method). The applicability of the proposed approach is demonstrated through an application to the post-earthquake performance assessment of the traffic network in Tangshan City, China, a city that suffered catastrophically from the 1976 Tangshan Earthquake. Analytical results show that the proposed method can well reflect both the temporal and the spatial variations of the traffic flow, and thus offers rational support for predicting the post-earthquake traffic scenarios and for optimizing strategies to improve the transportation capability under emergent conditions.

Transportation bridge infrastructure asset condition monitoring using uncrewed aerial vehicles (UAVs)

The optimal performance of a road transportation network depends on the serviceability and reliability of bridges connecting the pavements. Bridge performance depends on several geotechnical assets among which abutments play a key role as they support the bridge span, and transfer the vertical and horizontal loads from the superstructure to the foundation. Soil abutments are designed to be stable against overturning and sliding and prevent differential settlement and excessive lateral movements. In the United States, 42 % of bridges are observed to be over 50 years old. Department of Transportation (DOT) agencies periodically monitor and assess the bridge conditions typically every two years. Due to the costs and the time involved in conducting inspections using traditional data collection tools, DOTs are exploring innovative technologies to conduct inspections and realize benefits in terms of access, cost, and safety. They have widely adopted the use of uncrewed aerial vehicles (UAVs) for structural health monitoring of bridges. A few studies also conducted 360⁰ bridge inspections using camera sensors mounted on UAVs in the recent past. In this current study, the US Federal Aviation Administration (FAA) guidelines for commercial use of drones were discussed and followed in field studies. Further, UAVs were demonstrated for conducting 360° bridge inspections and identifying the erosion near abutments using three-dimensional models. This paper presents the application of innovative technologies for geotechnical assessments of two bridges in the Alaska region, with a focus on assessing the need for rehabilitation measures. These new technologies have become essential for reliable condition monitoring post-disaster events as well. This approach is expected to benefit the agencies in managing the transportation geotechnical assets in a timely and cost-effective manner.

Investigating optical transport network performance: A recurrence plot approach

Investigating optical transport network performance: A recurrence plot approach

Pre-disaster resource allocation based on network topology and flow features

Protecting transportation systems from severe damage is crucial for their post-disaster functionality. We adopt network topological metrics and generalised topological metrics involving demand and traffic flow characteristics to identify critical links for enhancing post-event transportation network performance. A single-parameter, non-monotonic function is proposed to allocate limited resources based on these metrics. Monte Carlo simulation generates failure scenarios to evaluate residual network performance through network topological measures and network level of service indicators. This work introduces ‘Trip Efficiency’, a novel level of service indicator that considers both demand connectivity and travel efficiency for a network with multiple links interrupted. In Anaheim case study network, betweenness-based resource allocation leads to better performance on network topological measures; while generalised topological metrics result in more connected OD pairs and higher travel efficiency. The experimental results demonstrate the effectiveness of the proposed method in enhancing network resilience against multisite disruptive events through proactive resource allocations.

Transportation Resilience Modeling and Bridge Reconstruction Planning Based on Time-Evolving Travel Demand during Post-Earthquake Recovery Period

After major earthquakes, communities may experience time-evolving population in terms of size and distribution, and varying travel demands, along with the displacement and recovery of residents caused by the damage and restoration of dwelling units. Community transportation can be significantly affected if the changes in population size and distribution are considerable. As a result, the post-earthquake infrastructure reconstruction process is essentially like urban replanning to meet the realistic traffic needs of the remaining and recovering residents and further maximize the sustainability of the community. To fill the gap in existing studies that considered the travel demand as fixed during the long-term recovery stage, it is important to investigate the effects of time-evolving travel demand on transportation resilience modeling and bridge reconstruction planning during the post-earthquake recovery period. A new methodology is proposed to analyze such impact by assessing the time-dependent resilience performance of transportation networks during the post-earthquake recovery stage. Traffic efficiency and safety are the two resilience performance indicators used to evaluate the transportation network. Post-earthquake infrastructure restoration planning is conducted using a heuristic algorithm based on the time-dependent resilience performance indicator. A demonstrative case study is carried out at Shelby County, Tennessee.

Travel Time Probability Prediction Based on Constrained LSTM Quantile Regression

Travel time reliability assessment has been widely used in recent years to evaluate the performance of transportation networks and measure the operation level of transportation systems. Weather, as one of the important factors influencing travel time reliability, affects the relationship between the supply and requirement of urban road networks. Considering the traffic characteristics under different traffic conditions, a study on the influence of weather on travel time reliability under different conditions is proposed to predict the probability of travelers completing their trips within the expected time under different weather conditions. Based on the urban road network data and cab trajectory data of Harbin city, this paper correlates the floating vehicle location with the road network information through a hidden Markov model to reduce the influence of vehicle trajectory errors on the calculation results of path travel time. To analyze the entire distribution of extreme travel time and its impact on the reliability of travel time under various traffic situations, it captures the tail features of the travel time distribution based on extreme value theory. Then, to increase the predictability of each quantile, it combines a deep-learning LSTM model and a quantile regression model to create a probabilistic travel time prediction model utilizing combined layers. The proposed model is compared with the linear quantile regression and neural network quantile regression models, and the model is evaluated in terms of point prediction results and probabilistic prediction results, respectively, to ensure the accuracy of predictions from the model. As a result, the prediction accuracy of the model in this paper is greatly improved, and the degree of violating quantile constraints is greatly reduced.

Betweenness Centrality-Based seismic risk management for bridge transportation networks

Although bridges are integral parts of transportation networks, they are vulnerable to natural hazards. The damage incurred during these natural hazards events leads to network disruptions and hinders post-event recovery. Therefore, improving the seismic performance of transportation networks is critical. This paper proposes an origin–destination pair betweenness centrality (BC)-based methodology to rank bridges according to their importance in a complex bridge network. The proposed methodology is an easy-to-use approach that considers user equilibrium and topology and significantly shorten the computational time. In addition, this study proposes two strategies including the retrofit of bridges and construction of new bridges based on the BC importance of bridges to mitigate the seismic risk of bride networks. A well-studied bridge network in California was used to demonstrate the application and the effect of different strategies was compared in this study. Bridges located at the shortest path significantly affect the seismic performance of bridge networks, indicating there is no need to rehabilitate or replace all bridges under budget constraints. Also, constructing five new bridges based on BC importance improved seismic performance of the bridge network much more than retrofitting 21 bridges. Moreover, the proposed methodology presents a significant reduction in computational time (only 1/30 compared with an existing study) in resolving the importance of bridges in a network and can be applied to provide useful information for disaster management teams to mitigate the seismic risk of bridge networks.

Decision support algorithm for intermodal transport networks: Urban aerial cableway systems

We propose a decision support algorithm to solve the service policy of urban cableway systems, which are used as a transport mode to address the First-Last Mile problem for intermodal urban public transport networks. Our work develops a stochastic discrete-event model for computing the queuing performance of the public transport networks and evaluates the effects on the passenger waiting time over a commercial working day, considering different service policies: (i) previous policy; (ii) current policy; and (iii) proposed policy. We provide an embarking procedure for urban cableway lines to aid transport operators in managing queues on platforms during rush hours. Finally, a set of large-scale Monte-Carlo simulations was conducted using a real multimodal network of a large-size metropolitan city (3.8 million inhabitants) to evaluate the effectiveness of different service policies.

Differential impacts of autonomous and connected-autonomous vehicles on household residential location

High market penetration of autonomous vehicles (AVs) and connected-autonomous vehicles (CAVs) is expected to impact transportation network performance, which is an important determinant of residential location decisions, especially for households who commute to work by personal vehicle. This study examines and compares the impacts of privately owned AVs and CAVs on the location and commute characteristics as well as the spatial distribution of households within the Triangle Region in North Carolina. A Mixed Multinomial Logit model is developed using recent household survey data to capture household preferences. In addition, the region’s travel demand model, the Triangle Regional Model, is used to predict the network-level impacts of AV and CAV adoption, and cluster analysis is conducted to explore how network performance changes vary with transportation demand and supply zone characteristics at a local and regional level. Residential location patterns are predicted for a number of AV and CAV scenarios for the year 2045 using the outputs of the econometric analysis and the Triangle Regional Model. We find that extensive adoption of private CAVs improves network conditions and encourages households to live farther from work, leading up to a 5.6% increase in suburban and rural households that commute to work by personal vehicles. A high market share of AVs is associated with deteriorated transportation network performance and up to a 2.8% increase in urban households. Results vary by market penetration rate of each technology, mix of AVs, CAVs, and human driven vehicles in the traffic stream, and fuel type (conventional-fuel versus electric vehicles).

Public transport equity with the concept of time-dependent accessibility using Geostatistics methods, Lorenz curves, and Gini coefficients

The primary objective of transport policies is to improve accessibility and mitigate the adverse consequences of private vehicles. Improved accessibility can considerably contribute to urban sustainability and quality of life. To improve accessibility, measuring it as precisely as possible is very important. A greater knowledge of the accessibility levels of public transport systems would not only help in enhancing the level of service but will also aid in resource planning and budgeting (capital costs, operations & maintenance costs, etc.). The research on various accessibility methods used to investigate transportation equity, particularly public transport, revealed that most of these approaches used time-based approaches to analyze public transport accessibility under theoretical traffic conditions and accessibility to public transport corridors, but ignored the overall performance of the transportation network. In this study, we developed an accessibility measurement framework for public transport to capture the performance of the entire transport network in a multimodal fashion and applied the methodology to the City of Wuhan as a case study. The key elements are: (i) develop a multimodal transport network that captures the performance of the entire transport network at a different time of the day. (ii) adopt the utility-based accessibility method to analyze public transport accessibility to opportunities based on the attractiveness of the destination, and (iii)determine the level of the disparity of accessing job opportunities by public transport between the urban and rural areas of the city using Geostatistics methods such as Lorenz curve and Gini Coefficients. The framework adopted in this study allowed us to examine the public transport equity in the study area, and help identify the influencing factors towards enhancing transport policies that consider the fair distribution of public transport infrastructure.

Scenario-Based Strategic Modeling of Road Transport Demand and Performance

Road infrastructure plays an important role in the economic and social development of societies. Thus, it requires effective and timely responses to cope with and embrace uncertain future changes. This study proposes an integrated, scenario-based strategic model which estimates transport demand, network performance, emission, and energy consumption. The model accounts for future economic, behavioral, policy, and technological developments by including four future scenarios and traces the possible changes in transport infrastructure performance. It primarily aims at supporting infrastructure decisions by quantifying a set of network and environmental performance indicators along their spatial–temporal dimension. Moreover, the effects of transport demand and energy transition policies on road performance are considered within each scenario. This model is applied in a case study region covering the main industrial and urban regions of the Netherlands. The discussed results provide insights into the possible infrastructure investments within each scenario and elaborate on the possible effects of policies.

Study on the Evolution and Coupling Coordinated Development of Passenger and Freight Transport Network of New Western Land-Sea Corridor

Based on the associated network of passenger and freight transport in the New Western Land-Sea Corridor, the structure and characteristics of passenger and freight transport network in different years are analyzed using passenger volume and freight volume, and the resilience of the network is analyzed using the disturbance model. Finally, the coordinated development of passenger transport and logistics is discussed by applying a coupling coordination model. The results show that, from 2000 to 2018, the New Western Land-Sea Corridor has realized the evolution of the spatial pattern of passenger transport network from “two center and numerous branches” to “three centers and three auxiliaries”. Moreover, the evolution of the spatial pattern of the freight transport network changed from “two centers and one auxiliary” to “one center and multiple auxiliaries”. In terms of network tenacity, the disturbance strategy based on node degree value has the strongest damage to the performance of passenger and freight transport networks. The coupling coordination value is gradually rising from a low level and finally develops to an ordered trend. Therefore, it is proposed that passenger transport development could be supported from the “growth pole” and “zone” development modes, while the logistics development would adopt the “axis” development pattern and focus on the protection of the vital city nodes in the corresponding network. In this way, it can realize the effective connection of multiple paths, improve the tenacity of network and boost hub economy, consummate the regional coordination mechanism, and finally promote the advancement of the New Western Land-Sea Corridor.

A Simulation-Based Model for Continuous Network Design Problem Using Bayesian Optimization

This paper investigates the continuous network design problem (CNDP) and proposes a simulation-based bi-level model and solution framework based on Bayesian optimization. In the bi-level model, the upper level minimizes the total system cost, and the lower level assigns traffic under an approximated dynamic equilibrium condition corresponding to the given network design strategy. A simulation tool integrated micro- and macro- traffic dynamics, namely SUMO, is employed to solve the lower-level problem. The embedded high-fidelity, non-linear understanding of traffic in the simulator oftentimes gains additional complexity due to the lack of tractable mathematical representation. Thus, a Bayesian machine learning technique is utilized to build the link between simulation and optimization. The proposed solution framework takes both the advantages of fine-grained simulations and the efficiency of surrogate-based optimization techniques. Moreover, lane width expansion is innovatively proposed as the decision variable of CNDP to bridge the gap between theory and practice. The relationship between link free-flow speed (FFS) and lane width is also explored based on real data and established to accurately calibrate the simulation input. For demonstrative purposes, numerical experiments on the optimal lane width expansion design were conducted in the inner city of Suzhou, China. The results show that with proper parameter settings, the proposed method is capable to find the global optimal solution within a very tight computational budget, which makes the simulation-based framework an encouraging option for policymakers to enhance transportation network performance.

How Intermediate Capacity Modes Provide Accessibility and Resilience in Metropolitan Transit Networks: Insights from a Global Study of 19 Cities

Drawing on the Spatial Network Analysis of Multimodal Urban Transport Systems (SNAMUTS) accessibility tool, this paper introduces comparative results of public transport network performance measures in 19 metropolitan regions in developed countries. These results are assessed typologically and functionally to highlight the contribution of each common public transport mode to maximize (or not) the integration of transport networks with the urban structure to optimize accessibility outcomes. It is shown that the capacity and performance spectrum embodied by each mode represents a gradual scale that allocates a specific niche to intermediate modes, particularly trams that are present in half the cities studied and absent from the others.In a comparison of Munich, Germany, where a full spectrum of public transport modes is present, and Hamburg, Germany, where there is a performance gap between heavy rail and buses, accessibility outcomes are discussed. Alongside “alternative history” scenarios concerning the hypothetical retention of trams in Hamburg and full closure of the system in Munich, it is shown that the absence of an intermediate mode in Hamburg’s actual network has a significant detrimental effect on the resilience of the public transport system compared to its Bavarian counterpart as well as to other international cities.

Performance Optimisation of Public Transport Networks Using AHP-Dependent Multi-Aspiration-Level Goal Programming

This study proposes an optimisation approach to improve multiple-criteria aspiration-level public transportation performance by combining public transport criteria matrix analytic hierarchy process (PTCM-AHP) models and multi-aspiration-level goal programming. The approach uses the PTCM-AHP to calculate the system weights. Based on the weight values, the approach combines the multi-aspiration goal-level selection process in three different ways. The proposed approach was used to optimise public transportation networks in Bayswater, Cockburn, and Stonnington, Australia, to demonstrate the public transportation network performance optimisation process. By controlling the criteria goal value interval, this new approach combines decision-making plans and strategies to optimise various scenarios. The optimisation outcomes can be applied to provide guidelines for improving the performance of public transportation networks.

Freeway Incident Diversionary Behavior as a Measure of Transportation Network Resilience

Traffic flow disruptions caused by road incidents reduce the performance of transportation networks. Under such conditions, drivers often attempt to reduce delay by diverting to alternate paths, redistributing trips to less congested routes. In the absence of route guidance information, the speed and efficiency of network recovery are most significantly influenced by driver familiarity with a network and, in particular, its daily variability. The goal of this study was to propose metrics and a general formula to relate driver diversionary behavior during incidents to assess resulting actions and conditions from the perspective of network resiliency. Another key objective of the work was to assess the applicability of routine traffic volume data collected by the Louisiana Department of Transportation and Development (LA DOTD) for assessing both driver adaptation and local network resiliency during incidents. The findings from the study suggest that resiliency during incident conditions is not a fixed value for a facility but varies depending on the severity of the incident, opportunities for travel diversion, and the actions of network managers.

Distributed Urban Freeway Traffic Optimization Considering Congestion Propagation

Traffic optimization strategies are imperative for improving the performance of transportation networks. Most traffic optimization strategies only depend on traffic states of congested road segments, where congestion propagation is neglected. Therefore, we propose a distributed traffic optimization strategy for urban freeways considering the potential congested road segments caused by congestion propagation, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">potential-homogeneous-area</i> (PHA). Utilizing the historical traffic density data, we first quantify the effect of congestion propagation and identify PHA by applying the proposed spatiotemporal lambda-connectedness method. Meanwhile, dynamic capacity constraints of PHA are determined and are integrated with the cell transmission model (CTM) in a centralized traffic optimization problem. To reduce computational complexity and improve scalability, we then propose a double-consensus-based alternating direction method of multipliers algorithm (DC-ADMM) to handle the neighbor coupling constraints and global coupling constraints for solving the problem in a fully distributed way. We prove that the proposed DC-ADMM algorithm converges to the optimal solution in the condition of a convex objective function. Finally, simulations based on real traffic density data, collected in Inner Ring Road, Shanghai, China, reveal the effectiveness of our proposed strategy.

Does transportation network centrality determine housing price?

In this paper, we investigate whether transportation network centrality determines housing price in cities. We find that it does. Using housing price data from >400 neighborhoods across the city of Kolkata, India, our research shows that a neighborhood's centrality within the intra-urban road transportation network is positively associated with the average price per sq. ft. of ownership units in multistory apartment buildings in the neighborhood. We test the relationships of three alternative centrality indices – closeness, betweenness, and eigenvector – capturing different dimensions of network-wide connectedness with housing price, independently and in combination. We employ two alternative network weights to derive centrality considering peak-period and off-peak travel conditions and road transportation network performance. We address the spatial autocorrelation issue to derive robust evidence on the centrality-price relationship. Our results suggest that centrality is an intrinsic location advantage that positively influences urban housing price. Different centrality indices have different effects on price but they collectively reinforce each other. The estimated magnitudes of association between centrality indices and housing price are significant for policy and practice. In addition to contributing to scholarship in the domain of transportation planning, our research offers specific takeaways for metropolitan planning agencies and real-estate developers, especially in resource constrained geographic contexts.