Planning for the unexpected: The value of reserve capacity for public transport network robustness

Public transport networks (PTN) are subject to recurring service disruptions. Most studies of the robustness of PTN have focused on network topology and considered vulnerability in terms of connectivity reliability. While these studies provide insights on general design principles, there is lack of knowledge concerning the effectiveness of different strategies to reduce the impacts of disruptions. This paper proposes and demonstrates a methodology for evaluating the effectiveness of a strategic increase in capacity on alternative PTN links to mitigate the impact of unexpected network disruptions. The evaluation approach consists of two stages: identifying a set of important links and then for each identified important link, a set of capacity enhancement schemes is evaluated. The proposed method integrates stochastic supply and demand models, dynamic route choice and limited operational capacity. This dynamic agent-based modelling of network performance enables to capture cascading network effects as well as the adaptive redistribution of passenger flows. An application for the rapid PTN of Stockholm, Sweden, demonstrates how the proposed method could be applied to sequentially designed scenarios based on their performance indicators. The method presented in this paper could support policy makers and operators in prioritizing measures to increase network robustness by improving system capacity to absorb unexpected disruptions.     

Identifying critical road segments and measuring system-wide robustness in transportation networks with isolating links: A link-based capacity-reduction approach

A wide range of relatively short-term disruptive events such as partial flooding, visibility reductions, traction hazards due to weather, and pavement deterioration occur on transportation networks on a daily basis. Despite being relatively minor when compared to catastrophes, these events still have profound impacts on traffic flow. To date there has been very little distinction drawn between different types of network-disruption studies and how the methodological approaches used in those studies differ depending on the specific research objectives and on the disruption scenarios being modeled.In this paper, we advance a methodological approach that employs different link-based capacity-disruption values for identifying and ranking the most critical links and quantifying network robustness in a transportation network. We demonstrate how an ideal capacity-disruption range can be objectively determined for a particular network and introduce a scalable system-wide performance measure, called the Network Trip Robustness (NTR) that can be used to directly compare networks of different sizes, topologies, and connectivity levels.Our approach yields results that are independent of the degree of connectivity and can be used to evaluate robustness on networks with isolating links. We show that system-wide travel-times and the rank-ordering of the most critical links in a network can vary dramatically based on both the capacity-disruption level and on the overall connectivity of the network. We further show that the relationships between network robustness, the capacity-disruption level used for modeling, and network connectivity are non-linear and not necessarily intuitive. We discuss our findings with respect to Braess’ Paradox.     

Exposing the role of exposure: Public transport network risk analysis

Network risk assessment takes into consideration the probability that adverse events occur and the impacts of such disruptions on network functionality. In the context of transport networks, most studies have focused on vulnerability, the reduction in performance indicators given that a disruption occurs. This study presents and applies a method to explicitly account for exposure in identifying and evaluating link criticality in public transport networks. The proposed method is compared with conventional measures that lack exposure information. A criticality assessment is performed by accounting for the probability of a certain event occurring and the corresponding welfare loss. The methodology was applied for a multi-modal public transport network in the Netherlands where data concerning disruptions was available. The results expose the role of exposure in determining link criticality and overall network vulnerability. The findings demonstrate that disregarding exposure risks prioritizing links with high passenger volumes over links with a higher failure probability that are significantly more critical to network performance. The inclusion of exposure allows performing a risk analysis and has consequences on assessing mitigation measures and investment priorities.     

A framework for robustness analysis of road networks for short term variations in supply

There is a growing awareness that road networks, are becoming more and more vulnerable to unforeseen disturbances like incidents and that measures need to be taken in order to make road networks more robust. In order to do this the following questions need to be addressed: How is robustness defined? Against which disturbances should the network be made robust? Which factors determine the robustness of a road network? What is the relationship between robustness, travel times and travel time reliability? Which indicators can be used to quantify robustness? How can these indicators be computed? This paper addresses these questions by developing a consistent framework for robustness in which a definition, terms related to robustness, indicators and an evaluation method are included. By doing this, policy makers and transportation analyst are offered a framework to discuss issues that are related to road network robustness and vulnerability which goes beyond the disconnected definitions, indicators and evaluation methods used so far in literature. Furthermore, the evaluation method that is presented for evaluating the robustness of the road network against short term variations in supply (like incidents) contributes to the problem of designing robust road networks because it has a relatively short computation time and it takes spillback effects and alternative routes into account.     

The role of travel demand and network centrality on the connectivity and resilience of an urban street system

In the transportation literature, two major and parallel approaches exist to identify the critical elements of a transportation system. On the one hand, conventional transportation engineering emphasizes travel demand, often in terms of traffic volume (i.e., demand side). On the other hand, newer techniques from Network Science emphasize network topology (i.e., supply side). To better understand the relationship between the two approaches, we first investigate whether they correlate by comparing traffic volume and node centrality. Second, we assess the impact of the two approaches on the connectivity and resilience of a transportation network; connectivity is measured by the relative size of the giant component, and resilience is measured by the network’s adaptive capacity (the amount of extra flow it can handle). The urban road system of Isfahan (Iran) is used as a practical case study. Overall, we find that traffic volume indeed correlates with node centrality. In addition, we find that the weighted degree of a node, i.e., the sum of the capacities of its incident links (for small disruptions) and node betweenness (for large disruptions), best captures node criticality. Nodes with high weighted degree and betweenness should therefore be given higher priority to enhance connectivity and resilience in urban street systems. Regarding link criticality, roads with higher capacities showed a more important role as opposed to betweenness, flow, and congestion.

     

Emergence of resilience as a framework for state Departments of Transportation (DOTs) in the United States

State Departments of Transportation (DOTs) in the United States are responsible for a large portfolio of transportation modes and services, including passenger and freight systems. These responsibilities include operations under routine conditions and during incidents and events that result from various natural and human-caused hazards. During unexpected events, disruptions and reductions in service result in requiring the reallocation and reassignment of personnel, modal, and economic resources. To better prevent and respond to the effects of service disruptions, the concept of resilience has emerged as an important framework, within which, DOTs across the United States are using to plan for the occurrence of threats. In this paper, the key findings of recent reviews of literature and practice related to resilience among state DOTs in the United States are summarized. The review effort focused on a range of risks faced by transportation agencies including climate change, terrorism, cyber-attacks, and aging infrastructure and the ways in which DOTs are confronting them in practice. The topics of this paper range from the fundamental, including definitions of transportation resilience; to the more complex such as examinations of risk, vulnerability and threats; to the most sophisticated topics including administrative-level efforts to conceptualize evolving transportation planning and policies within a resilience framework.     

Evaluating locational accessibility to the US air transportation system

Although there are hundreds of airports that support commercial air passenger traffic in the United States (US), not all areas are equivalently served by the commercial air transportation system. Locations in the US differ with respect to their level of access to the commercial air network and their overall accessibility within the system. Given the complexity of the domestic commercial air passenger network and supporting infrastructure, past research has only been able to provide a limited assessment of locational accessibility within the United States. To address these complexities, this paper proposes a new metric that incorporates measures of access to air transport as well as accessibility within air transportation networks. Using a comprehensive dataset on scheduled airline service, the developed approach is then applied to the US domestic commercial passenger air transportation network to explore geographic differentials in accessibility. Results suggest marked differences between core-based statistical areas throughout the US.     

Measuring post-disaster transportation system performance: the 1995 Kobe earthquake in comparative perspective

Recent earthquake disasters have caused major damage to transportation networks, leading to significant economic disruption. While this suggests the need to evaluate total system performance in transportation risk assessment, in addition to examining the vulnerability of individual components such as bridges, no appropriate measures currently exist. This paper develops post-disaster system performance measures and applies them to the urban rail and highway transportation systems in the Kobe, Japan, region devastated by the 1995 Hyogoken–Nanbu earthquake. Performance is evaluated in terms of network coverage and transport accessibility. Performance degradation was much more severe for highways and railways than for other lifeline infrastructure systems. Both transportation systems fared poorly in the disaster but service restoration proceeded much more rapidly for rail. The restoration of highway system performance correlated closely with the recovery of highway traffic volumes. The paper further develops a measure of subarea transport accessibility and applies this to Kobe’s constituent city wards. Results indicate substantial spatial disparity that is maintained throughout the restoration period. Comparisons with the 1989 Loma Prieta and 1994 Northridge earthquakes in the US show that although these disasters caused notable damage to highway bridges, system performance degradation was small in comparison with the Kobe experience. The paper argues that explicitly measuring transportation system performance can greatly facilitate both understanding the effects of historic disasters and preparing for future hazard events.     

Estimating economic losses of industry clusters due to port disruptions

Seaport operations are highly important for industries which rely heavily on imports and exports. A reliable evaluation of port risks is essential to govern the normal running of seaborne transportation and thus the industrial economies. The occurrence of a breakdown in the trade facilitators, such as ports, will disrupt the smooth flow of supply chains for the industries. The estimation of the economic loss for an industry when a port gets disrupted is a challenging task as the relationship between the port and industry clusters is complex. This study aims to develop a systematic framework for performing economic loss estimation of industry clusters due to port disruptions. The whole risk assessment is split into three stages focusing on the establishment of a network flow model, economic estimations and evaluating risk mitigation strategies. The proposed idea is demonstrated by a case study on Shenzhen port and its related manufacturing industries. A dynamic inventory control strategy used by manufacturers is found to be beneficial for mitigating port disruption risks.     

Adaptive traffic signal control with actor-critic methods in a real-world traffic network with different traffic disruption events

The transportation demand is rapidly growing in metropolises, resulting in chronic traffic congestions in dense downtown areas. Adaptive traffic signal control as the principle part of intelligent transportation systems has a primary role to effectively reduce traffic congestion by making a real-time adaptation in response to the changing traffic network dynamics. Reinforcement learning (RL) is an effective approach in machine learning that has been applied for designing adaptive traffic signal controllers. One of the most efficient and robust type of RL algorithms are continuous state actor-critic algorithms that have the advantage of fast learning and the ability to generalize to new and unseen traffic conditions. These algorithms are utilized in this paper to design adaptive traffic signal controllers called actor-critic adaptive traffic signal controllers (A-CATs controllers).The contribution of the present work rests on the integration of three threads: (a) showing performance comparisons of both discrete and continuous A-CATs controllers in a traffic network with recurring congestion (24-h traffic demand) in the upper downtown core of Tehran city, (b) analyzing the effects of different traffic disruptions including opportunistic pedestrians crossing, parking lane, non-recurring congestion, and different levels of sensor noise on the performance of A-CATS controllers, and (c) comparing the performance of different function approximators (tile coding and radial basis function) on the learning of A-CATs controllers. To this end, first an agent-based traffic simulation of the study area is carried out. Then six different scenarios are conducted to find the best A-CATs controller that is robust enough against different traffic disruptions. We observe that the A-CATs controller based on radial basis function networks (RBF (5)) outperforms others. This controller is benchmarked against controllers of discrete state Q-learning, Bayesian Q-learning, fixed time and actuated controllers; and the results reveal that it consistently outperforms them.     

Assessing potential likelihood and impacts of landslides on transportation network vulnerability

As one of the devastating natural disasters, landslide may induce significant losses of properties and lives area-wide, and generate dramatic damages to transportation network infrastructure. Accessing the impacts of landslide-induced disruptions to roadway infrastructure can be extremely difficult due to the complexity of involved impact factors and uncertainties of vulnerability related events. In this study, a data-driven approach is developed to assess landslide-induced transportation roadway network vulnerability and accessibility. The vulnerability analysis is conducted by integrating a series of static and dynamic factors to reflect the landslide likelihood and the consequences of network accessibility disruptions. The analytical hierarchy process (AHP) model was developed to assess and map the landslide likelihood. A generic vulnerability index (VI) was calculated for each roadway link in the network to identify critical links. Spatial distributions of landslide likelihood, consequences of network disruptions, and network vulnerability degrees were fused and analyzed. The roadway network on Oahu Island in Hawaii is utilized to demonstrate the effectiveness of the proposed approach with all the geo-coded information for its network vulnerability analysis induced by area-wide landslides. Specifically, the study area was classified into five categories of landslide likelihood: very high, high, moderate, low, and stable. About 34% of the study area was assigned as the high or very high categories. The results of network vulnerability analyses highlighted the importance of three highway segments tunnel through the Ko‘olau Range from leeward to windward, connecting Honolulu to the windward coast including the Pali highway segment, Likelike highway segment, and Interstate H-3 highway segment. The proposed network vulnerability analysis method provides a new perspective to examine the vulnerability and accessibility of the roadway network impacted by landslides.     

A multi-criteria decision support methodology for implementing truck operation strategies

This study proposes a multi-criteria decision support methodology to enable the prioritization of potential alternative transportation system operations strategies and then demonstrates the effectiveness of the methodology using a case study involving truck operations. The primary feature of this methodology is its ability to help policymakers consider economic, public, and private sector standpoints simultaneously. The economic criterion is cost to the public sector where four criteria related to truck impacts on the transportation system are incorporated. These are traffic congestion, safety hazards, air pollution, and pavement damage. In addition, reliability and productivity are regarded as metrics representing the private sector viewpoint since they can significantly affect profitability. The methodology combines qualitative and quantitative aspects of these standpoints. In order to demonstrate the applicability of this methodology, a corridor with some of the highest truck traffic in the US is selected as a case study and three forms of left lane restrictions for trucks are considered. For qualitative analysis, survey data were collected from two groups classified as public agency and transportation industry professionals who are experts in trucking. In addition, a micro traffic simulation model was used to produce various performance measurements that can describe quantitative impacts. As a result, the methodology provides a rational argument for prioritizing potential alternative truck strategies.     

An intersection turning movement estimation procedure based on path flow estimator

Estimation of intersection turning movements is one of the key inputs required for a variety of transportation analysis, including intersection geometric design, signal timing design, traffic impact assessment, and transportation planning. Conventional approaches that use manual techniques for estimation of turning movements are insensitive to congestion. The drawbacks of the manual techniques can be amended by integrating a network traffic model with a computation procedure capable of estimating turning movements from a set of link traffic counts and intersection turning movement counts. This study proposes using the path flow estimator, originally used to estimate path flows (hence origin–destination flows), to derive not only complete link flows, but also turning movements for the whole road network given some counts at selected roads and intersections. Two case studies using actual traffic counts are used to demonstrate the proposed intersection turning movement estimation procedure. Copyright © 2010 John Wiley & Sons, Ltd.     

Strategies for customer service level protection under multi-echelon supply chain disruption risk

We model a multi-echelon system where disruptions can occur at any stage and evaluate multiple strategies for protecting customer service if a disruption should occur. The strategies considered take advantage of the network itself and include satisfying demand from an alternate location in the network, procuring material or transportation from an alternate source or route, and holding strategic inventory reserves throughout the network. Unmet demand is modeled using a mix of backordering and lost sales. We conduct numerical analysis and provide recommendations on selecting strategic mitigation methods to diminish the impact of disruptions on customer service. We demonstrate that the greatest service level improvements can be made by providing both proactive inventory placement to cover short disruptions or the start of long disruptions, and reactive back-up methods to help the supply chain recover after long or permanent disruptions.     

An RFID-based inventory management framework for emergency relief operations

In the aftermath of super storm Sandy, a large region from North Carolina to Maine endured food shortages, power outages, and long lines at gas stations forced to ration fuel due to low supply and high demand. These issues were largely the result of the affected transportation network’s inability to effectively cope with random and highly dynamic changes, and a lack of available resources and suppliers who were capable of enacting adequate emergency response measures. These problems experienced during super storm Sandy further underscored the need for a robust emergency inventory management system, where planning policies can be integrated with real-time on-line inventory management strategies to keep track of fluctuations of vital commodities such as food, water, medicine, fuel and power supplies. Motivated by this important problem, this paper investigates a comprehensive feedback-based emergency management framework for disasters such as super storm Sandy that provides integration with an emerging intelligent transportation systems technology, namely Radio Frequency Identification Devices (RFID). Within this framework, the offline-planning problem is solved by the stochastic humanitarian inventory management approach; and the online modeling strategy includes the application of a continuous time model predictive control technique. After introducing the mathematical background, the proposed framework is discussed using case studies built based on super storm Sandy in order to understand the efficiency and practicality of this RFID-based methodology. Results suggest that the methodology can properly account for and react to the rapidly changing needs for vital supplies that occur during the emergency relief operations. Based on this approach, planners and decision makers can be aware of the time delay that can happen due to disaster-related disruptions and thus maintain a safe level of buffer for vital supplies.     

Mitigation of disruptions in public transit by Bee Colony Optimization

Dispatchers in many public transit companies face the daily problem of assigning available buses to bus routes under conditions of bus shortages. In addition to this, weather conditions, crew absenteeism, traffic accidents, traffic congestion and other factors lead to disturbances of the planned schedule. We propose the Bee Colony Optimization (BCO) algorithm for mitigation of bus schedule disturbances. The developed model takes care of interests of the transit operator and passengers. The model reassigns available buses to bus routes and, if it is allowed, the model simultaneously changes the transportation network topology (it shortens some of the planned bus routes) and reassigns available buses to a new set of bus routes. The model is tested on the network of Rivera (Uruguay). Results obtained show that the proposed algorithm can significantly mitigate disruptions.     

基于模块化的综合性绿色交通考核评价体系构建及动态作用机理研究

绿色交通发展是适应新时代发展要求、交通运输行业实现节能环保健康持续发展的有力支撑。本文基于已有的绿色交通相关考核评价体系,采用模块化理论,着眼于绿色交通的政府、企业、社会三种对象,形成适用性强、针对性突出的模块化考核评价体系。     

Multi-scale perimeter control approach in a connected-vehicle environment

This paper proposes a novel approach to integrate optimal control of perimeter intersections (i.e. to minimize local delay) into the perimeter control scheme (i.e. to optimize traffic performance at the network level). This is a complex control problem rarely explored in the literature. In particular, modeling the interaction between the network level control and the local level control has not been fully considered. Utilizing the Macroscopic Fundamental Diagram (MFD) as the traffic performance indicator, we formulate a dynamic system model, and design a Model Predictive Control (MPC) based controller coupling two competing control objectives and optimizing the performance at the local and the network level as a whole. To solve this highly non-linear optimization problem, we employ an approximation framework, enabling the optimal solution of this large-scale problem to be feasible and efficient. Numerical analysis shows that by applying the proposed controller, the protected network can operate around the desired state as expressed by the MFD, while the total delay at the perimeter is minimized as well. Moreover, the paper sheds light on the robustness of the proposed controller. This multi-scale hybrid controller is further extended to a stochastic MPC scheme, where connected vehicles (CV) serve as the only data source. Hence, low penetration rates of CVs lead to strong noises in the controller. This is a first attempt to develop a network-level traffic control methodology by using the emerging CV technology. We consider the stochasticity in traffic state estimation and the shape of the MFD. Simulation analysis demonstrates the robustness of the proposed stochastic controller, showing that efficient controllers can indeed be designed with this newly-spread vehicle technology even in the absence of other data collection schemes (e.g. loop detectors).     

Estimating historical hourly traffic volumes via machine learning and vehicle probe data: A Maryland case study

This paper focuses on the problem of estimating historical traffic volumes between sparsely-located traffic sensors, which transportation agencies need to accurately compute statewide performance measures. To this end, the paper examines applications of vehicle probe data, automatic traffic recorder counts, and neural network models to estimate hourly volumes in the Maryland highway network, and proposes a novel approach that combines neural networks with an existing profiling method. On average, the proposed approach yields 24% more accurate estimates than volume profiles, which are currently used by transportation agencies across the US to compute statewide performance measures. The paper also quantifies the value of using vehicle probe data in estimating hourly traffic volumes, which provides important managerial insights to transportation agencies interested in acquiring this type of data. For example, results show that volumes can be estimated with a mean absolute percent error of about 21% at locations where average number of observed probes is between 30 and 47 vehicles/h, which provides a useful guideline for assessing the value of probe vehicle data from different vendors.     

Estimating spatial interdependence in automobile type choice with survey data

In this article, we show that vehicle type ownership is spatially dependent at both the regional and household-level even after controlling for income and population density. We discuss reasons for the existence of spatial effects in vehicle ownership, and note potential implications for policymakers. Our results point to the importance of spatial relationships in transportation research and highlight the hazards of ignoring their role in affecting transportation outcomes. For example, if vehicle type choice is affected by neighborhood spillovers, agencies that regulate traffic flow and road safety could tailor their choice projections and policy tools to account for such interdependence.