Model and a solution algorithm for the dynamic resource allocation problem for large-scale transportation network evacuation

Allocating movable resources dynamically enables evacuation management agencies to improve evacuation system performance in both the spatial and temporal dimensions. This study proposes a mixed integer linear program (MILP) model to address the dynamic resource allocation problem for transportation evacuation planning on large-scale networks. The proposed model is built on the earliest arrival flow formulation that significantly reduces problem size. A set of binary variables, specifically, the beginning and the ending time of resource allocation at a location, enable a strong formulation with tight constraints. A solution algorithm is developed to solve for an optimal solution on large-scale network applications by adopting Benders decomposition. In this algorithm, the MILP model is decomposed into two sub-problems. The first sub-problem, called the restricted master problem, identifies a feasible dynamic resource allocation plan. The second sub-problem, called the auxiliary problem, models dynamic traffic assignment in the evacuation network given a resource allocation plan. A numerical study is performed on the Dallas–Fort Worth network. The results show that the Benders decomposition algorithm can solve an optimal solution efficiently on a large-scale network.     

Critical success conditions of collaborative methods: a comparative evaluation of transport planning projects

This paper explores critical success conditions of collaborative planning projects in the area of urban transport, evaluating the impact of new collaborative methods, instruments and processes on project performance. Hypothesis building is based on a comparative, empirical research design, rather than on deductive theory construction. Potential critical success conditions are derived from literature. Based on five urban transport planning projects in Gothenburg (Sweden), London (United Kingdom), Milwaukee (United States), Tokyo (Japan) and Mexico City (Mexico), a rough set analysis of the five cases reveals validated success conditions, which can be used for formulating hypotheses for further research or for policy and process improvement. The results suggest that a dedicated management of the multi-actor network, a high diversity of actors, as well as an extensive use of knowledge integration methods in combination with a high network density are critical success conditions of these planning processes. Surprisingly, the extensive use of unilateral methods also showed to be an important success condition. The traditional role of the planner will have to be complemented with the expertise of network and methodology management. The authors conclude that rough set analysis can be a valuable addition to narrative, single-case analysis of collaborative urban transport planning processes.     

Optimization of transport investment and pricing policies: the role of transport pricing in network design

This paper investigates the role of transport pricing in network design and describes two facts about flow pattern in a transportation system. The first, illustrated by an example of Braess paradox, is that adding a new link to the network does not necessarily minimize the total travel time. The second is that introducing of appropriate toll pricing may reduce not only the total network time but also the travel time for each individual traveller. It follows with the investigations of different system objectives and different pricing policies (only toll pricing and distance‐based pricing are considered), and shows how they affect the system performance and flow pattern. Lastly, a systematic optimization process is proposed for integrated planning of transport network and pricing policies.     

System dynamics of urban traffic based on its parking-related-states

The urban parking and the urban traffic systems are essential components of the overall urban transportation structure. The short-term interactions between these two systems can be highly significant and influential to their individual performance. The urban parking system, for example, can affect the searching-for-parking traffic, influencing not only overall travel speeds in the network (traffic performance), but also total driven distance (environmental conditions). In turn, the traffic performance can also affect the time drivers spend searching for parking, and ultimately, parking usage. In this study, we propose a methodology to model macroscopically such interactions and evaluate their effects on urban congestion.The model is built on a matrix describing how, over time, vehicles in an urban area transition from one parking-related state to another. With this model it is possible to estimate, based on the traffic and parking demand as well as the parking supply, the amount of vehicles searching for parking, the amount of vehicles driving on the network but not searching for parking, and the amount of vehicles parked at any given time. More importantly, it is also possible to estimate the total (or average) time spent and distance driven within each of these states. Based on that, the model can be used to design and evaluate different parking policies, to improve (or optimize) the performance of both systems.A simple numerical example is provided to show possible applications of this type. Parking policies such as increasing parking supply or shortening the maximum parking duration allowed (i.e., time controls) are tested, and their effects on traffic are estimated. The preliminary results show that time control policies can alleviate the parking-caused traffic issues without the need for providing additional parking facilities. Results also show that parking policies that intend to reduce traffic delay may, at the same time, increase the driven distance and cause negative externalities. Hence, caution must be exercised and multiple traffic metrics should be evaluated before selecting these policies.Overall, this paper shows how the system dynamics of urban traffic, based on its parking-related-states, can be used to efficiently evaluate the urban traffic and parking systems macroscopically. The proposed model can be used to estimate both, how parking availability can affect traffic performance (e.g., average time searching for parking, number of cars searching for parking); and how different traffic conditions (e.g., travel speed, density in the system) can affect drivers ability to find parking. Moreover, the proposed model can be used to study multiple strategies or scenarios for traffic operations and control, transportation planning, land use planning, or parking management and operations.     

Rationing and pricing strategies for congestion mitigation: Behavioral theory,econometric model,and application in Beijing

Some travel demand management policies such as road pricing have been widely studied in literature. Rationing policies, including vehicle ownership quota and vehicle usage restrictions, have been implemented in several megaregions to address congestion and other negative transportation externalities, but not well explored in literature. Other strategies such as Vehicle Mileage Fee have not been well accepted by policy makers, but attract growing research interest. As policy makers face an increasing number of policy tools, a theoretical framework is needed to analyze these policies and provide a direct comparison of their welfare implications such as efficiency and equity. However, such a comprehensive framework does not exist in literature. To bridge this gap, this study develops an analytical framework for analyzing and comparing travel demand management policies, which consists of a mathematical model of joint household vehicle ownership and usage decisions and welfare analysis methods based on compensating variation and consumer surplus. Under the assumptions of homogenous users and single time period, this study finds that vehicle usage rationing performs better when relatively small percentages of users (i.e. low rationing ratio) are rationed off the roads and when induced demand elasticity resulting from congestion mitigation is low. When the amount of induced demand exceeds a certain level, it is shown analytically that vehicle usage restrictions will always cause welfare losses. When the policy goal is to reduce vehicle travel by a fixed portion, road pricing provides a larger welfare gain. The performance of different policies is influenced by network congestion and congestibility. This paper further generalizes the model to consider heterogenous users and demonstrates how it can be applied for policy analysis on a real network after careful calibration.     

Modeling dry-port-based freight distribution planning

In this paper we review the dry port concept and its outfalls in terms of optimal design and management of freight distribution. Some optimization challenges arising from the presence of dry ports in intermodal freight transport systems are presented and discussed. Then we consider the tactical planning problem of defining the optimal routes and schedules for the fleet of vehicles providing transportation services between the terminals of a dry-port-based intermodal system. An original service network design model based on a mixed integer programming mathematical formulation is proposed to solve the considered problem. An experimental framework built upon realistic instances inspired by regional cases is described and the computational results of the model are presented and discussed.     

The effects of the 2013 floods on Germany’s freight traffic

This article analyzes the spatio-temporal effect of the 2013 floods on freight traffic in Germany by using automatic traffic counter data. The methodology uses a proven time-series outlier detection and identification technique to endogenously determine if a counter was affected during the flood and estimate the magnitude and duration of the change in the number of vehicles passing through it. This is the first paper able to quantify climate-related variations in traffic across all the counters of a national network. Results show variations on 10% of all counters and 23% of all main roads. Results allow us to trace the configuration of disrupted and detour routes, recovery times, and the total effect on the network. Our findings serve as an input to other studies on the impact of exogenous events on the transport system and contribute towards the formulation of public policies to improve road resilience.     

Service network design with asset management: Formulations and comparative analyses

In this paper, we address the service network design with asset management problem, which integrates asset management considerations into service network design models for consolidation-based freight carriers. We propose model formulations based on arc variables for both flow and design, as well as formulations with path flow variables and new cycle design variables. Problem instances reflecting actual planning problems are used in the computational study to analyze the strengths and weaknesses of the various model formulations and the impact of asset management considerations on the transportation plan and the computational effort. Experimental results indicate that formulations based on cycle variables outperform traditional arc-based formulations, and that considering asset management issues may significantly impact the outcome of service planning models.     

Conflict-free arrival and departure trajectory planning for parallel runway with advanced point-merge system

In this paper, an efficient trajectory planning system is proposed to solve the integration of arrivals and departures on parallel runways with a novel route network system. Our first effort is made in designing an advanced Point Merge (PM) route network named Multi-Level Point Merge (ML-PM) to meet the requirements of parallel runway operations. Then, more efforts are paid on finding a complete and efficient framework capable of dynamically modelling the integration of arrival and departure trajectories on parallel runways, modelling the conflict detection and resolution in presence of curved trajectory and radius-to-fix merging process. After that, a suitable mathematical optimization formulation is built up. Receding Horizon Control (RHC) and Simulated Annealing (SA) algorithms are proposed to search the near-optimal solution for the large scale trajectories in routine dense operations. Taking Beijing Capital International Airport (BCIA) as a study case, the experimental results show that our system shows good performances on the management of arrivals and departures. It can automatically solve all the potential conflicts in presence of dense traffic flows. With its unique ML-PM route network, it can realize a shorter flying time and a near-Continuous Descent Approach (CDA) descent for arrival aircraft, an economical climbing for departure aircraft, an easier runway allocation together with trajectory control solutions. It shows a good and dynamic sequencing efficiency in Terminal Manoeuvring Area (TMA). In mixed ML-PM mode, under tested conditions, our proposed system can increase throughput at BCIA around 26%, compared with baseline. The methodology defined here could be easily applied to airports worldwide.     

Development and Deployment of Public Transport Policy and Planning in Taiwan

Over the past decade, the escalating roadway congestion and environmental deterioration due to heavy use of private vehicles have provoked the Taiwan government to realize the importance of public transport systems. Under the “carrot-and-stick” rationales, the government has formulated public transport policies and exercised a series of related initiatives by providing sufficient and higher quality of public transport services so as to attract more private vehicle users. In this paper, the planning philosophy and policy formulation of Taiwan’s public transport development are highlighted. The most important initiatives, including the Five-year Enhancement of Mass Transportation Program and the upgrading public transport schemes in the National Development Plan are examined. Based on previous experience and lessons, we point out the most challenging issues that the government will encounter. Suggestions for the future of public transport planning are also addressed.     

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.     

A network option portfolio management framework for adaptive transportation planning

A real option portfolio management framework is proposed to make use of an adaptive network design problem developed using stochastic dynamic programming methodologies. The framework is extended from Smit’s and Trigeorgis’ option portfolio framework to incorporate network synergies. The adaptive planning framework is defined and tested on a case study with time series origin-destination demand data. Historically, OD time series data is costly to obtain, and there has not been much need for it because most transportation models use a single time-invariant estimate based on deterministic forecasting of demand. Despite the high cost and institutional barriers of obtaining abundant OD time series data, we illustrate how having higher fidelity data along with an adaptive planning framework can result in a number of improved management strategies. An insertion heuristic is adopted to run the lower bound adaptive network design problem for a coarse Iran network with 834 nodes, 1121 links, and 10 years of time series data for 71,795 OD pairs.     

Testing and evaluation of a multi-commodity multi-modal network flow model for disaster relief management

The results of the testing of an optimization model in disaster relief management are presented. The problem is a large-scale multi-commodity, multi-modal network flow problem with time windows. Due to the nature of this problem, the size of the optimization model grows extremely rapidly as the number of modes and/or commodities increase. The formulation is based on the concept of a time-space network. Two heuristic algorithms are developed. One exploits an inherent network structure of the problem with a set of side constraints and the other is an interactive fix-and-run heuristic. The findings of the model-testing and a wide range of sensitivity analyses using an artificially generated data set are presented. Both solution procedures prove to be efficient and effective in providing close to optimal solutions.     

A decomposition approach to determining fleet size and structure with network flow effects and demand uncertainty

This paper presents a new methodology to determine fleet size and structure for those airlines operating on hub‐and‐spoke networks. The methodology highlights the impact of stochastic traffic network flow effects on fleet planning process and is employed to construct an enhanced revenue model by incorporating the expected revenue optimization model into fleet planning process. The objective of the model is to find a feasible allocation of aircraft fleet types to route legs using minimum fleet purchasing cost, thus ensuring that the expected fleet profit is maximized subject to several critical resource constraints. By using a linear approximation to the total network revenue function, the fleet planning model with enhanced revenue modeling is decomposed into the nonlinear aspects of expected revenue optimization and the linear aspects of determining fleet size and structure by optimal allocation of aircraft fleet types to route legs. To illustrate this methodology and its economic benefits, an example consisting of 6 chosen aircraft fleet types, 12 route legs, and 57 path‐specific origin‐destination markets is presented and compared with the results found using revenue prorated fleet planning formulation. The results show that the fleet size and structure of the methodology proposed in this paper gain 211.4% improvement in fleet profit over the use of the revenue prorated fleet planning approach. In addition, comparison with the deterministic model reveals that the fleet size and structure of this proposed methodology are more adaptable to the fluctuations of passenger demands. Copyright © 2016 John Wiley & Sons, Ltd.     

Models for optimizing transportation networks and modal split in China

This paper summarizes the research in a project entitled “The Models for Optimizing Transportation Network and Modal Split in China”. The research background, procedure, various mathematical models used in traffic demands forecasting, modal split and network design are presented with the key results. The systematic optimization approach adopted in this paper for integrated planning of transport network and the rational modal split formulation is firstly proposed in China. Finally, further discussion on the difficulties of using transport modeling techniques in Chinese conditions is given.     

Campus parking supply impacts on transportation mode choice

Parking demand is a significant land-use problem in campus planning. The parking policies of universities and large corporations with facilities located in small urban areas shape the character of their campuses. These facilities will benefit from a simplified methodology to study the effects of parking availability on transportation mode mix and impacts on recruitment and staffing policies. This paper, based on a case study of North Dakota State University in the United States, introduces an analytical framework to provide planners with insights about how parking supply and demand affects campus transportation mode choice. The methodology relies only on aggregate mode choice data for the special generator zone and the average aggregate volume/capacity ratio projections for all external routes that access the zone. This reduced data requirement significantly lowers analysis cost and obviates the need for specialized modelling software and spatial network analysis tools. Results illustrate that the framework is effective for analysing mode choice changes under different scenarios of parking supply and population growth.     

Multi-period planning of closed-loop supply chain with carbon policies under uncertainty

Climate change and greenhouse gases emissions have caused countries to implement various carbon regulatory mechanisms in some industrial sectors around the globe to curb carbon emissions. One effective method to reduce industry environmental footprint is the use of a closed-loop supply chain (CLSC). The decision concerning the design and planning of an optimal network of the CLSC plays a vital role in determining the total carbon footprint across the supply chain and also the total cost. In this context, this research proposes an optimization model for design and planning a multi-period, multi-product CLSC with carbon footprint consideration under two different uncertainties. The demand and returns uncertainties are considered by means of multiple scenarios and uncertainty of carbon emissions due to supply chain related activities are considered by means of bounded box set and solve using robust optimization approach. The model extends further to investigate the impact of different carbon policies such as including strict carbon cap, carbon tax, carbon cap-and-trade, and carbon offset on the supply chain strategic and operational decisions. The model captures trade-offs that exist among supply chain total cost and carbon emissions. Also, the proposed model optimizes both supply chain total cost and carbon emissions across the supply chain activities. The numerical results reveal some insightful observations with respect to CLSC strategic design decisions and carbon emissions under various carbon policies and at the end we highlighted some managerial insights.     

Things planners believe in,and things they deny

The paper examines the economic theory of consumer behavior from the Modern Psychoanalytic point of view with a metaphor of (transport) planning as a backdrop. It is claimed that the utility maximization principle of economic theory, no matter how broadly interpreted, does not envelop the motivation of human behavior and that the economic explanation of the aims of human behavior, which disregards the "dark side" of man, will result in narrowly conceived plans and policies.The application of economic theory, and other social sciences, to public policy has led to disjointedness of data collection, method of analysis, and planning and implementation. This, combined with a tautological behavioral theory, has made the current planning and public policy procedures self-verifying, and issues an invitation for a new theoretical approach which is comprehensive, does not ignore complexity and the large "error term", and appreciates the unity of data collection, method of investigation, and process of implementation.It is argued that unsatisfactory performance of the transport system, large errors inherent in data and models, the inability of extant transportation planning and policy to address key issues successfully, the environmental problem, and the value of the individual all call for new kinds of skills from the planners and policy makers.Freud's dualistic formulation of instinctual drives -- the life and death instincts -- is used as the new framework of explanation. This theory is seen to offer a convincing, more realistic and deeper understanding of the forces motivating human behavior, and one which is more in accordance with observable facts. If applied, the theory will lead to significantly different planning processes and to a profoundly different approach to public policy.     

广州市道路交通规划建设探讨

文章针对广州市道路系统规划的基本状况及存在的问题,结合国外城市道路交通规划建设及管理经验,提出了基于小尺度道路网、合理道路等级级配和快速路系统的城市道路交通规划建设优化措施及建议,为缓解城市交通拥堵提供理论依据。     

The rationality of transport investment packages

Investment packages, involving several modes, emerge as a widely used element in urban transport policies. Package policies entail some specific possibilities and traps in the planning and decision-making processes. To assess these properties, the concepts of instrumental and communicative rationality are applied. These rationality concepts serve to structure the pro and con arguments concerning four crucial considerations in package planning: The use of formal impact assessment methods, the overall size and changeability of the investment packages, the share of road vs green modes investments, and the sequence of implementation. These issues are investigated by drawing on experience from transport investment packages in six Norwegian cities.