Large Transportation Networks Research Articles

Exploring the effects of public transport subsidies on satisfaction and ridership

The determinants of public transport demand are multifaceted, necessitating consideration of various factors that extend beyond the objective aspects of operation. In addition to travel time and costs, it is imperative to comprehend psychosocial factors influencing public transport usage to enhance policies geared toward fostering ridership and sustainable mobility. Empirical evidence suggests that subjective attributes, such as satisfaction and attitudes, exert a discernible influence on travel behavior and the utilization of public transport. Nevertheless, these linkages remain largely unexplored, especially within the context of large public transport networks in cities of the Global South. This paper aims to address this gap by evaluating the impact of demand-side subsidies in the form of travel vouchers on public transport ridership to understand the mediating effect of satisfaction levels according to the users’ socioeconomic characteristics. These satisfaction levels are related to overall satisfaction with transport, employment and educational opportunities, and life. To achieve this, a large-scale randomized controlled experiment was conducted in Bogotá, Colombia, involving a sample of frequent public transport users (N = 1,607). The participants were divided into a treatment group, which received the travel vouchers loaded onto their smart travel cards for four months, and a control group. Hybrid-ordered logit models were employed to estimate the average number of weekly trips undertaken before, during, and after the intervention. The result shows an increase in ridership by the treatment group, which does not solely depend on improved affordability; but is influenced by a complex interplay of socioeconomic factors and shifts in satisfaction levels. This confirmed that travel satisfaction can contribute to increased public transport demand.

Networkwide Traffic State Forecasting Using Exogenous Information: A Multi-Dimensional Graph Attention-Based Approach

Traffic-state forecasting is crucial for traffic management and control strategies, as well as user- and system-level decision-making in the transportation network. While traffic forecasting has been approached with a variety of techniques over the last couple of decades, most approaches simply rely on endogenous traffic variables for state prediction, despite the evidence that exogenous factors can significantly affect traffic conditions. This paper proposes a multidimensional spatiotemporal graph attention-based traffic-prediction approach (M-STGAT), which predicts traffic based on past observations of speed, along with lane-closure events, temperature, and visibility across a large transportation network. The approach is based on a graph attention network architecture, which learns based on the structure of the transportation network on which these variables are observed. Numerical experiments are performed using traffic-speed and lane-closure data from the Caltrans Performance Measurement System (PeMS) and corresponding weather data from the National Oceanic and Atmospheric Administration (NOOA) Automated Surface Observing Systems (ASOS). The numerical experiments implement three alternative models which do not allow for multidimensional input, along with two alternative multidimensional models, based on the literature. The M-STGAT outperforms the five alternative models in validation and testing with the primary data set, as well as for one transfer data set across all three prediction horizons for all error measures. However, the model’s transferability varies for the remaining two transfer data sets, which may require further investigation. The results demonstrate that M-STGAT has the most consistently low error values across all transfer data sets and prediction horizons.

Integrated Bus Timetabling and Scheduling with a Mutation-Based Evolutionary Scheme Maximizing Headway Quality and Connections

Public transport planning is a multi-level process that includes various complex tasks. These tasks are traditionally executed sequentially, and the result of each task serves as input for consecutive tasks. A simultaneous integrated consideration of multiple tasks may lead to an overall improved solution, but further increase the complexity of already hard-to-solve planning problems. This work focuses on timetabling and vehicle scheduling and evaluates synergies from the integrated optimization. We investigate an exact sequential, exact integrated, and heuristic approach to solve the combined problem for large public transport networks considering the interlining of vehicles, multiple vehicle types, or multiple depots while additionally aiming to maximize regular “clock-faced’’ headways and transfer connections. Compared to sequential optimization, an integrated approach significantly reduces nominal and operational costs while maintaining high service quality. However, an exact integrated approach is only able to compute solutions for problems of limited size in a reasonable time. We propose an adaptive modular evolutionary extendable scheme that effectively balances computational efficiency and solution quality. By utilizing various problem-specific mutation operators and adaptively applying them based on their impact, the heuristic can compute high-quality solutions for large real-world-inspired public transport networks in a reasonable time while considering short connecting times between lines and regular clock-faced headways.

SPASTC: a Spatial Partitioning Algorithm for Scalable Travel-time Computation

Travel-time computation with large transportation networks is often computationally intensive for two main reasons: 1) large computer memory is required to handle large networks; and 2) calculating shortest-distance paths over large networks is computing intensive. Therefore, previous research tends to limit their spatial extent to reduce computational intensity or resolve computational intensity with advanced cyberinfrastructure. In this context, this article describes a new Spatial Partitioning Algorithm for Scalable Travel-time Computation (SPASTC) that is designed based on spatial domain decomposition with computer memory limit explicitly considered. SPASTC preserves spatial relationships required for travel-time computation and respects a user-specified memory limit, which allows efficient and large-scale travel-time computation within the given memory limit. We demonstrate SPASTC by computing spatial accessibility to hospital beds across the conterminous United States. Our case study shows that SPASTC achieves significant efficiency and scalability making the travel-time computation tens of times faster.

Fighting for Routes: Resource Allocation among Competing Planners in Transportation Networks

In transportation networks, incomplete information is ubiquitous, and users often delegate their route choice to distributed route planners. To model and study these systems, we introduce network control games, consisting of multiple actors seeking to optimise the social welfare of their assigned subpopulations through resource allocation in an underlying nonatomic congestion game. We first analyse the inefficiency of the routing equilibria by calculating the Price of Anarchy for polynomial cost functions, and then, using an Asynchronous Advantage Actor–Critic algorithm implementation, we show that reinforcement learning agents are vulnerable to choosing suboptimal routing as predicted by the theory. Finally, we extend the analysis to allow vehicles to choose their route planner and study the associated equilibria. Our results can be applied to mitigate inefficiency issues arising in large transport networks with route controlled autonomous vehicles.

An empirical study of large transportation networks and solutions for the cost optimization

Abstract We all know how important transportation is nowadays - from one street to another, from one city to another, from one county to another, from one region to another, from one country to another, from one continent to another and this list can go on with examples. The question we ask ourselves is: Can we find a better way? A better solution? A more accurate way? A more efficient, faster route to travel? All things take more or less time. All journeys matter. Whether you’re going for a walk, on a trip, stuck in traffic on your way to work, have a small business and want to save money on bills, or you are one of the world’s biggest entrepreneurs, for all of us, every second matters. That is why our transport, or our things, goods and so on, must be as efficient, accurate, correctly planned as possible, but at the same time safe. Nowadays, when everything is in a very accelerated continuous evolution, the efficiency and speed of achieving our goals are very important. There is a lot of emphasis on as little wasted time as possible and as many successes as possible. The expected productivity can only be achieved if all transport features are worked out with great care. Every little detail must be considered, mathematically, computationally, logically and chronologically, so that the overall result is the best, the most suitable, the most efficient for our problem. This work aims to study some examples of transport networks, following their advantages and limitations, and looks for all kinds of solutions, ideas to improve and optimize their costs. We tested two existing applications on some large datasets (Section 2 and Section 3).

Partial and complete replanning of an intermodal logistic system under disruptions

The exclusive and excessive use of long-distance road transportation is not suitable way to reduce the negative environmental impacts of logistics systems. Intermodal transport, combining road with other transport modes, has the potential to reduce both operating costs and carbon footprints. One of the reasons for the low share of intermodal transport is its requirement for the coordination of scheduled transport services that can result in reducing reliability in case of disruptions due to the arrival of new shipment orders, fluctuations in shipment quantities, delays, and service cancellations within the network. This calls for reliable and efficient algorithms to replan the shipments’ distribution. In this paper, the replanning problem is formulated as a path-based multi-commodity network flows. We provide two different network topologies, one of which is based on a time–space network, while the other embeds time aspect in a highly scalable alternative structure to large transportation networks. We propose a column generation method whose pricing sub-problems are presented as resource constrained shortest path problem solved via a tailored label-correcting algorithm. We look at the pros and cons of complete and partial replanning in case of disruption and provide managerial insights for intermodal networks. An extensive set of computational experiments is presented on realistic instances being generated with the consultation of our industrial partners for a logistic network including railways, waterways, and roads. The promising outcomes validate the efficiency of the proposed approach that can be easily adjusted to real-time intermodal logistic replanning.

Simplified approach to integrate seismic retrofitting prioritization with social cost evaluation: A case study in central Italy

In the last three decades, bridge stock seismic retrofitting prioritization has become one of the cult topics for scientific discussions in the bridge management strategies. More recent methods are focusing on the evaluation of the generalized failure cost, of a specific bridge derived from direct and indirect costs induced to the users/residents of the area exposed to the seismic hazard as a consequence of bridge collapse. However, when these approaches have to be applied to large transport networks, appear still very complex and computational demanding, and therefore simplified methods to evaluate the impact in terms of social cost related to the reduced efficiency of a transportation network due to potential bridge failure, are required. In this work, a simplified method for seismic retrofitting prioritization on a bridge stock is proposed, which is based on a “blended” approach considering specific fragility curves according to several bridge features and condition state, seismic inputs and generalized failure costs related to the transportation network. The effectiveness of the method has been showed on a case study of a local bridge stock placed in central Italy and the obtained results have been compared with those provided by more refined transport simulation models, on one hand, and by more traditional prioritization approaches, on the other. It is highlighted that this method can be very useful for transportation network managers with in a limited budget scenario, in case of lack of information about possible earthquake-induced impacts on a transportation network efficiency. • Introducing an updated review on bridge retrofitting prioritization methods. • Studying a new approach to evaluate earthquake impact on transportation networks. • Focusing on costs related to transportation networks earthquake induced disruptions. • Applying a simplified prioritization method that can be easily implemented. • Providing a sound and simple prioritization approach in limited budget scenarios.

Area-wide traffic signal control based on a deep graph Q-Network (DGQN) trained in an asynchronous manner

Value-based reinforcement learning (RL) algorithms have been widely applied in traffic signal studies. There are, however, several problems in jointly controlling traffic lights for a large transportation network. First, the discrete action space exponentially explodes as the number of intersections to be jointly controlled increases. With its model structure, the original deep Q-network (DQN) could not accommodate a large action space. The problem was resolved by revising the output structure of a DQN holding the framework of a single-agent RL algorithm Second, when mapping traffic states into an action value, it is difficult to consider spatio-temporal correlations over a large transportation network. A deep graph Q-network (DGQN) was devised to efficiently accommodate spatio-temporal dependencies on a large scale. Finally, training the proposed DGQN with a large number of joint actions requires much time to converge. An asynchronous update methodology with multiple actor learners was devised for a DGQN to quickly reach an optimal policy. By combining these three remedies, a DGQN succeeded in jointly controlling the traffic lights in a large transportation network in Seoul. This approach outperformed other “state-of-the-art” RL algorithms as well as an actual fixed-signal operation. The proposed DGQN decreased the average delay of the current fixed operation to 55.7%, whereas those of reference models DQN-OGCN and DQN-FC were 72.5 and 92.0%, respectively.

Monitoring deformations of infrastructure networks: A fully automated GIS integration and analysis of InSAR time-series

Ageing stock and extreme weather events pose a threat to the safety of infrastructure networks. In most countries, funding allocated to infrastructure management is insufficient to perform systematic inspections over large transport networks. As a result, early signs of distress can develop unnoticed, potentially leading to catastrophic structural failures. Over the past 20 years, a wealth of literature has demonstrated the capability of satellite-based Synthetic Aperture Radar Interferometry (InSAR) to accurately detect surface deformations of different types of assets. Thanks to the high accuracy and spatial density of measurements, and a short revisit time, space-borne remote-sensing techniques have the potential to provide a cost-effective and near real-time monitoring tool. Whilst InSAR techniques offer an effective approach for structural health monitoring, they also provide a large amount of data. For civil engineering procedures, these need to be analysed in combination with large infrastructure inventories. Over a regional scale, the manual extraction of InSAR-derived displacements from individual assets is extremely time-consuming and an automated integration of the two datasets is essential to effectively assess infrastructure systems. This paper presents a new methodology based on the fully automated integration of InSAR-based measurements and Geographic Information System-infrastructure inventories to detect potential warnings over extensive transport networks. A Sentinel dataset from 2016 to 2019 is used to analyse the Los Angeles highway and freeway network, while the Italian motorway network is evaluated by using open access ERS/Envisat datasets between 1992 and 2010, COSMO-SkyMed datasets between 2008 and 2014 and Sentinel datasets between 2014 and 2020. To demonstrate the flexibility of the proposed methodology to different SAR sensors and infrastructure classes, the analysis of bridges and viaducts in the two test areas is also performed. The outcomes highlight the potential of the proposed methodology to be integrated into structural health monitoring systems and improve current procedures for transport network management.

How Cellular Agriculture Systems Can Promote Food Security

Cellular agriculture, the manufacturing of animal-sourced foods by cell cultures, may promote food security by providing a food source that is available, accessible, utilized, and stable. The extent to which cellular agriculture can promote food security, however, will depend in part on the supply system by which it produces food. Many cellular agriculture companies appear poised to follow a centralized supply system, in which production is concentrated within a small number of large plants and products are distributed over a wide area. This model benefits from economies of scale, but has several weaknesses to food security. By being built of a handful of plants with products distributed by a large transportation network, the centralized model is vulnerable to closures, as became clear for animal-sourced centralized system during the COVID-19 pandemic. Cellular agriculture systems are being built now; therefore, alternative supply system models of decentralized and distributed systems should be considered as the systems of cellular agriculture production are established. This paper defines both the requirements of food security and three possible supply system models that cellular agriculture could take and evaluates each model based on the requirements of food security.

Identifying critical corridors during an area-wide disruption by evaluating network bottleneck capacity

This paper applies the min-cut max-flow theorem combined with a grid cell disruption method over a large transportation network, to identify the importance of network locations in providing travel capacity when a community is evacuating (say, from wildfire). We develop metrices that look at the importance and contribution of individual links to network bottleneck capacity in traveling from the evacuating community to the shelter community. The purpose of this is to determine the network location that is the most restrictive of all, and more importantly, where it is in reference to the evacuating community location. We apply this method to the highway network of Alberta, Canada, with evacuating communities identified as those that have been under wildfire threat historically and/or are expected to in the future. We find that in all cases, network locations that contribute the greatest share of bottleneck capacity, are located adjacent to these evacuating communities. Next, we look at combining the measure for several communities, finding that the highways in remote northern Alberta are important despite some of them having lower capacity than the multi-lane highways in the south. Application of this simple method can support provincial and local municipal governments in deciding which communities require more detailed emergency evacuation studies to better identify and communicate transportation network deficiencies to provincial and federal bodies that would be making infrastructure investments towards community health and resilience.

Risk-averse optimization for resilience enhancement of complex engineering systems under uncertainties

With the growth of complexity and extent, large scale interconnected network systems, e.g., transportation networks or infrastructure networks, become more vulnerable to external disturbances. Hence, managing potential disruptive events during the design, operating, and recovery phase of an engineered system and therefore improving the system’s resilience is an important yet challenging task. To ensure system resilience after the occurrence of failure events, this study proposes a mixed-integer linear programming (MILP) based restoration framework using heterogeneous dispatchable agents. The scenario-based stochastic optimization (SO) technique is adopted to deal with the inherent uncertainties imposed on the recovery process from nature. Moreover, different from conventional SO using deterministic equivalent formulations, the CVaR risk measure is implemented for this study because of the temporal sparsity of the decision making in applications such as the recovery from extreme events. The resulting restoration framework involves a large-scale MILP problem and thus an adequate decomposition technique i.e. modified Lagrangian dual decomposition, is also employed to achieve tractable computational complexity. Case study results based on the IEEE 37-bus test feeder demonstrate the benefits of using the proposed framework for resilience improvement as well as the advantages of adopting SO formulations.

Why is my bus suddenly so crowded? Spillover effects of the discontinuation of three-in-one policy in Jakarta

We assess the case of the abrupt discontinuation of the three-in-one policy, a high-occupancy vehicle (HOV) restriction, in Jakarta, with the objective of mapping potential interdependencies in the transportation system. Statistical investigation of the passenger volume in the bus rapid transit (BRT) system in the whole city before and after the policy change revealed a significant increase in the number of passengers during peak hours, especially in the evening period. The extent of the increase, however, depended on whether the area had been subject to the initial policy restriction. The case of sudden discontinuation of the three-in-one policy in Jakarta illustrates how a change in policy aimed at a single transportation mode may spill over to alternative transportation modes. The importance of acknowledging the systemic nature of urban transportation systems when altering policies intended to discourage the use of a single transportation mode within the larger transportation network is discussed.

Graph-based ahead monitoring of vulnerabilities in large dynamic transportation networks.

Betweenness Centrality (BC) has proven to be a fundamental metric in many domains to identify the components (nodes) of a system modelled as a graph that are mostly traversed by information flows thus being critical to the proper functioning of the system itself. In the transportation domain, the metric has been mainly adopted to discover topological bottlenecks of the physical infrastructure composed of roads or railways. The adoption of this metric to study the evolution of transportation networks that take into account also the dynamic conditions of traffic is in its infancy mainly due to the high computation time needed to compute BC in large dynamic graphs. This paper explores the adoption of dynamic BC, i.e., BC computed on dynamic large-scale graphs, modeling road networks and the related vehicular traffic, and proposes the adoption of a fast algorithm for ahead monitoring of transportation networks by computing approximated BC values under time constraints. The experimental analysis proves that, with a bounded and tolerable approximation, the algorithm computes BC on very large dynamically weighted graphs in a significantly shorter time if compared with exact computation. Moreover, since the proposed algorithm can be tuned for an ideal trade-off between performance and accuracy, our solution paves the way to quasi real-time monitoring of highly dynamic networks providing anticipated information about possible congested or vulnerable areas. Such knowledge can be exploited by travel assistance services or intelligent traffic control systems to perform informed re-routing and therefore enhance network resilience in smart cities.

A computationally efficient metric for identification of critical links in large transportation networks

Maintaining safety and resilience in transportation system requires identification of critical components. The existing transportation link criticality metrics typically require multiple traffic assignments to perform a full scan of the network. Link removals create computational burden and can cause network disconnectivity which makes it problematic to run traffic assignment. The authors previously introduced Link Criticality Index (LCI) that identifies the criticality ranking within a single User Equilibrium (UE) traffic assignment using Frank-Wolfe (FW) algorithm. While LCI is shown to provide balanced rankings with respect to connectivity and flow conditions, its computational efficiency diminishes for larger networks due to the need for path enumeration. This paper formulates an adjusted LCI to make it computationally efficient for larger networks. Adjusted LCI utilizes 1) viable routes instead of the complete list of enumerated paths; and, 2) a path-based traffic assignment algorithm (Gradient Projection) to replace FW. The consistency of rankings between original/adjusted LCIs, and other measures from the literature are analyzed and compared for three networks, and through an experimental setup. The proposed method is also applied on a large-scale transportation network, i.e., Chicago network. The results show that both modifications are effective and yield faster results without compromising LCI's advantages.

A bilevel approach for the collaborative transportation planning problem

The integration of the outbound and the inbound logistics of a company leads to a large transportation network, allowing to detect backhauling opportunities to increase the efficiency of the transportation. In collaborative networks, backhauling is used to find profitable services in the return trip to the depot and to reduce empty running of vehicles. This work investigates the vertical collaboration between a shipper and a carrier for the planning of integrated inbound and outbound transportation. Based on the hierarchical nature of the relation between the shipper and the carrier and their different goals, the problem is formulated as a bilevel Vehicle Routing Problem with Selective Backhauls (VRPSB). At the upper level, the shipper decides the minimum cost delivery routes and the set of incentives offered to the carrier to perform integrated routes. At the lower level, the carrier decides which incentives are accepted and on which routes the backhaul customers are visited. We devise a mathematical programming formulation for the bilevel VRPSB, where the routing and the pricing problems are optimized simultaneously, and propose an equivalent reformulation to reduce the problem to a single-level VRPSB. The impact of collaboration is evaluated against non-collaborative approaches and two different side payment schemes. The results suggest that our bilevel approach leads to solutions with higher synergy values than the approaches with side payments.

Assessing the Serviceability Status of Existing Transport Network by Using Geospatial Techniques in Metropolitan City Lahore


 Reliable and an efficient Public Transport plays key role in establishing sustainable Urban Transport system and healthy environment in any city. Public Transport equity in terms of easy access for the commuters is very crucial. With the drastic increase in population and cities extent in developing countries like Pakistan, the travel demand is recklessly increasing which urge to provide efficient public transit which may cater the ever increasing transport demand. Lahore being the provincial capital of Punjab and the second most densely populated city of Pakistan, has a remarkably large transportation network. The case study presented an advanced approach to determine the population with walking access to transit stop of Lahore Public Transport network, by using Geographical Information System (GIS). This approach is quite efficient, reliable and helpful to investigate the effectiveness of transit network, user's access to transit stops and spatial gaps in the transit facilities. GIS-based network service area analysis has been utilized to find the transit stop service area of the existing public transport network in metropolitan city Lahore. To find the transit stop service area, suitable walking time of 8 min or threshold distance of 624 m with walking speed of 78 m/min has been used as a standard. Transit stop service area has been used to find the population with walking access to a transit stop. The results reveal that only 40% of the total population was in suitable walking distance, which shows that less serviceability is due to the improper spatial distribution of existing transit stops across the transit route. The results also identify the population which is not in suitable walking distance to transit service. This study concludes that Geospatial Techniques are significant in assessment of the effectiveness and subsequently measuring the gaps in the existing transport network. Thus, on the basis of these results sustainable solution would be presented to enhance the efficacy of the integrated public transport.
 

How does an open system bus rapid transit (BRT) facilitate inter and intra-modal mobility? A visual analytic analysis of Brisbane, Australia

This paper explores the multimodal mobility impact of implementing BRT as an open system with mixed services and transfers or as a closed system with separate BRT services. In large multimodal public transport networks, interchanges or transfers for passengers are inevitable. A zone-based fare system ensures there is no financial penalty for interchange thus minimising the interchange penalty. In Brisbane, South East Queensland, Australia, the zone-based fare system does not penalise transfers within the same zone but charges a full fare for an inter-zone transfer within a single journey. The open system BRT in Brisbane, Australia is used as a case study with a sensitivity analysis on transfer rates to understand how open system operation has facilitated multimodal mobility between conventional bus and BRT services. Web-based simulation maps compare a closed BRT scenario with the open BRT system in operation. The coverage and volume of passengers are significantly reduced in the closed system scenario, especially for routes inbound to the Central Business District (CBD), since far fewer passengers in suburban areas would have direct BRT services. In Brisbane's current open system, there are fewer opportunities for multimodal trips because buses travel on and off the BRT infrastructure. This analysis can help urban transport agencies to optimise their BRT operations and provide better public transport services.

Airport Connectivity Optimization for 5G Ultra-Dense Networks

The rapid increase of air traffic demand and complexity of radio access network motivate developing scalable wireless communications by adopting system intelligence. The lack of adaptive reconfiguration in radio transmission systems may cause dramatic impacts on the traffic management concerning congestion and demand-capacity imbalances. This has driven the industry to jointly access licensed and unlicensed bands for improved airport connectivity. Therefore, intelligent system is embedded into fifth generation (5G) ultra-dense networks (UDNs) to provision dense and irregular deployments that support extended coverage and improved energy-efficiency for the entire airport network at higher throughputs. To define the technical aspects of the proposed solution, this paper addresses new intelligent technique that configures the coverage and capacity factors of radio access network considering the changes in air traffic demands. This technique is analysed through mathematical models that employ power consumption constraints to support dynamic traffic control requirements to improve the overall network capacity. The presented problem is formulated for medium or large airport air transportation network. The power optimization problem is solved using linear programming with careful consideration to latency and energy efficiency factors. Specifically, an intelligent pilot power method is adopted to maintain the connectivity throughout multi-interface technologies by assuming minimum power requirements. Numerical and system-level analysis are conducted to validate the performance of the proposed schemes for both licenced macrocell New Radio (NR) and unlicensed wireless fidelity (WiFi) topologies. Finally, the insights of problem modelling with intelligent techniques provide significant advantages at reasonable complexity and brings the great opportunity to improve the airport network capacity.