Most Cited Transportation Research Part C: Emerging Technologies Articles
The most cited articles published since 2011, extracted from Scopus.
Statistical methods versus neural networks in transportation research: Differences, similarities and some insightsM. G. Karlaftis | E. I. Vlahogianni
Volume 19, Issue 3, June 2011, Pages 387-399
In the field of transportation, data analysis is probably the most important and widely used research tool available. In the data analysis universe, there are two 'schools of thought'; the first uses statistics as the tool of choice, while the second - one of the many methods from - Computational Intelligence. Although the goal of both approaches is the same, the two have kept each other at arm's length. Researchers frequently fail to communicate and even understand each other's work. In this paper, we discuss differences and similarities between these two approaches, we review relevant literature and attempt to provide a set of insights for selecting the appropriate approach. © 2010 Elsevier Ltd.
Electric vehicles: How much range is required for a day's driving?Nathaniel S. Pearre | Willett Kempton | Randall L. Guensler | Vetri V. Elango
Volume 19, Issue 6, December 2011, Pages 1171-1184
One full year of high-resolution driving data from 484 instrumented gasoline vehicles in the US is used to analyze daily driving patterns, and from those infer the range requirements of electric vehicles (EVs). We conservatively assume that EV drivers would not change their current gasoline-fueled driving patterns and that they would charge only once daily, typically at home overnight. Next, the market is segmented into those drivers for whom a limited-range vehicle would meet every day's range need, and those who could meet their daily range need only if they make adaptations on some days. Adaptations, for example, could mean they have to either recharge during the day, borrow a liquid-fueled vehicle, or save some errands for the subsequent day. From this analysis, with the stated assumptions, we infer the potential market share for limited-range vehicles. For example, we find that 9% of the vehicles in the sample never exceeded 100. miles in one day, and 21% never exceeded 150. miles in one day. These drivers presumably could substitute a limited-range vehicle, like electric vehicles now on the market, for their current gasoline vehicle without any adaptation in their driving at all. For drivers who are willing to make adaptations on 2. days a year, the same 100. mile range EV would meet the needs of 17% of drivers, and if they are willing to adapt every other month (six times a year), it would work for 32% of drivers. Thus, it appears that even modest electric vehicles with today's limited battery range, if marketed correctly to segments with appropriate driving behavior, comprise a large enough market for substantial vehicle sales. An additional analysis examines driving versus parking by time of day. On the average weekday at 5. pm, only 15% of the vehicles in the sample are on the road; at no time during the year are fewer than 75% of vehicles parked. Also, because the return trip home is widely spread in time, even if all cars plug in and begin charging immediately when they arrive home and park, the increased demand on the electric system is less problematic than prior analyses have suggested. © 2011 Elsevier Ltd.
Real-time road traffic prediction with spatio-temporal correlationsWanli Min | Laura Wynter
Volume 19, Issue 4, August 2011, Pages 606-616
Real-time road traffic prediction is a fundamental capability needed to make use of advanced, smart transportation technologies. Both from the point of view of network operators as well as from the point of view of travelers wishing real-time route guidance, accurate short-term traffic prediction is a necessary first step. While techniques for short-term traffic prediction have existed for some time, emerging smart transportation technologies require the traffic prediction capability to be both fast and scalable to full urban networks. We present a method that has proven to be able to meet this challenge. The method presented provides predictions of speed and volume over 5-min intervals for up to 1. h in advance. © 2010 Elsevier Ltd.
Smart card data use in public transit: A literature reviewMarie Pier Pelletier | Martin Trépanier | Catherine Morency
Volume 19, Issue 4, August 2011, Pages 557-568
Smart card automated fare collection systems are being used more and more by public transit agencies. While their main purpose is to collect revenue, they also produce large quantities of very detailed data on onboard transactions. These data can be very useful to transit planners, from the day-to-day operation of the transit system to the strategic long-term planning of the network. This review covers several aspects of smart card data use in the public transit context. First, the technologies are presented: the hardware and information systems required to operate these tools; and privacy concerns and legal issues related to the dissemination of smart card data, data storage, and encryption are addressed. Then, the various uses of the data at three levels of management are described: strategic (long-term planning), tactical (service adjustments and network development), and operational (ridership statistics and performance indicators). Also reported are smart card commercialization experiments conducted all over the world. Finally, the most promising research avenues for smart card data in this field are presented; for example, comparison of planned and implemented schedules, systematic schedule adjustments, and the survival models applied to ridership. © 2010 Elsevier Ltd.
Traffic congestion pricing methodologies and technologiesAndré de Palma | Robin Lindsey
Volume 19, Issue 6, December 2011, Pages 1377-1399
This paper reviews the methods and technologies for congestion pricing of roads. Congestion tolls can be implemented at scales ranging from individual lanes on single links to national road networks. Tolls can be differentiated by time of day, road type and vehicle characteristics, and even set in real time according to current traffic conditions. Conventional toll booths have largely given way to electronic toll collection technologies. The main technology categories are roadside-only systems employing digital photography, tag & beacon systems that use short-range microwave technology, and in-vehicle-only systems based on either satellite or cellular network communications. The best technology choice depends on the application. The rate at which congestion pricing is implemented, and its ultimate scope, will depend on what technology is used and on what other functions and services it can perform. © 2011 Elsevier Ltd.
Bus arrival time prediction at bus stop with multiple routesBin Yu | William H K Lam | Mei Lam Tam
Volume 19, Issue 6, December 2011, Pages 1157-1170
Provision of accurate bus arrival information is vital to passengers for reducing their anxieties and waiting times at bus stop. This paper proposes models to predict bus arrival times at the same bus stop but with different routes. In the proposed models, bus running times of multiple routes are used for predicting the bus arrival time of each of these bus routes. Several methods, which include support vector machine (SVM), artificial neural network (ANN), k nearest neighbours algorithm (k-NN) and linear regression (LR), are adopted for the bus arrival time prediction. Observation surveys are conducted to collect bus running and arrival time data for validation of the proposed models. The results show that the proposed models are more accurate than the models based on the bus running times of single route. Moreover, it is found that the SVM model performs the best among the four proposed models for predicting the bus arrival times at bus stop with multiple routes. © 2011 Elsevier Ltd.
A safety-based approaching behavioural model with various driving characteristicsWuhong Wang | Wei Zhang | Hongwei Guo | Heiner Bubb | Katsushi Ikeuchi
Volume 19, Issue 6, December 2011, Pages 1202-1214
In order to understand driver's safety-related approaching behaviour during car-following process in more depth, it is necessary to achieve the comprehensive analysis of vehicle-to-vehicle dynamic interactions. Based on qualitative description of driving shaping behaviour associated with driving human factors of influencing driver's car-following behaviour, this paper presents briefly the fundamentals of simulation modelling of driver's safety approaching behaviour in urban traffic operation. The emphasis on our research is placed on the development of a driver's safety approaching behavioural model by considering the variability of following vehicle's speed to the leading vehicle's one and the relative distances among vehicles. Furthermore, we have carried out simulation and analysis of driver's deceleration and acceleration behaviour under different driving situations after identified the key safety-related parameters. Finally the developed model has been validated by using detailed vehicle trajectory data that was collected in naturalistic driving environment. The results show that the safety-based approaching behavioural model could be used to analyze driver's car-following behaviour for driving support and to reveal the essence of traffic flow characteristics at the microscopic level. © 2011 Elsevier Ltd.
Speed models for energy-efficient maritime transportation: A taxonomy and surveyHarilaos N. Psaraftis | Christos A. Kontovas
Volume 26, Issue , January 2013, Pages 331-351
International shipping accounts for 2.7% of worldwide CO2 emissions, and measures to curb future emissions growth are sought with a high sense of urgency. With the increased quest for greener shipping, reducing the speed of ships has obtained an increased role as one of the measures to be applied toward that end. Already speed has been important for economic reasons, as it is a key determinant of fuel cost, a significant component of the operating cost of ships. Moreover, speed is an important parameter of the overall logistical operation of a shipping company and of the overall supply chain and may directly or indirectly impact fleet size, ship size, cargo inventory costs and shippers' balance sheets. Changes in ship speed may also induce modal shifts, if cargo can choose other modes because they are faster. However, as emissions are directly proportional to fuel consumed, speed is also very much connected with the environmental dimension of shipping. So when shipping markets are in a depressed state and " slow-steaming" is the prevalent practice for economic reasons, an important side benefit is reduced emissions. In fact there are many indications that this practice, very much applied these days, will be the norm in the future. This paper presents a survey of speed models in maritime transportation, that is, models in which speed is one of the decision variables. A taxonomy of such models is also presented, according to a set of parameters. © 2012 Elsevier Ltd.
Tramp ship routing and scheduling with speed optimizationInge Norstad | Kjetil Fagerholt | Gilbert Laporte
Volume 19, Issue 5, August 2011, Pages 853-865
Tramp shipping companies are committed to transport a set of contracted cargoes and try to derive additional revenue from carrying optional spot cargoes. Traditionally, models for ship routing and scheduling problems are based on fixed speed and a given fuel consumption rate for each ship. However, in real life a ship's speed is variable within an interval, and fuel consumption per time unit can be approximated by a cubic function of speed. Here we present the tramp ship routing and scheduling problem with speed optimization, where speed on each sailing leg is introduced as a decision variable. We present a multi-start local search heuristic to solve this problem. To evaluate each move in the local search we have to determine the optimal speed for each sailing leg of a given ship route. To do this we propose two different algorithms. Extensive computational results show that the solution method solves problems of realistic size and that taking speed into consideration in tramp ship routing and scheduling significantly improves the solutions. © 2010 Elsevier Ltd.
Real time queue length estimation for signalized intersections using travel times from mobile sensorsXuegang Jeff Ban | Peng Hao | Zhanbo Sun
Volume 19, Issue 6, December 2011, Pages 1133-1156
We study how to estimate real time queue lengths at signalized intersections using intersection travel times collected from mobile traffic sensors. The estimation is based on the observation that critical pattern changes of intersection travel times or delays, such as the discontinuities (i.e., sudden and dramatic increases in travel times) and non-smoothness (i.e., changes of slopes of travel times), indicate signal timing or queue length changes. By detecting these critical points in intersection travel times or delays, the real time queue length can be re-constructed. We first introduce the concept of Queue Rear No-delay Arrival Time which is related to the non-smoothness of queuing delay patterns and queue length changes. We then show how measured intersection travel times from mobile sensors can be processed to generate sample vehicle queuing delays. Under the uniform arrival assumption, the queuing delays reduce linearly within a cycle. The delay pattern can be estimated by a linear fitting method using sample queuing delays. Queue Rear No-delay Arrival Time can then be obtained from the delay pattern, and be used to estimate the maximum and minimum queue lengths of a cycle, based on which the real-time queue length curve can also be constructed. The model and algorithm are tested in a field experiment and in simulation. © 2011 Elsevier Ltd.
Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networksYu Wei | Mu Chen Chen
Volume 21, Issue 1, April 2012, Pages 148-162
Short-term passenger flow forecasting is a vital component of transportation systems. The forecasting results can be applied to support transportation system management such as operation planning, and station passenger crowd regulation planning. In this paper, a hybrid EMD-BPN forecasting approach which combines empirical mode decomposition (EMD) and back-propagation neural networks (BPN) is developed to predict the short-term passenger flow in metro systems. There are three stages in the EMD-BPN forecasting approach. The first stage (EMD Stage) decomposes the short-term passenger flow series data into a number of intrinsic mode function (IMF) components. The second stage (Component Identification Stage) identifies the meaningful IMFs as inputs for BPN. The third stage (BPN Stage) applies BPN to perform the passenger flow forecasting. The historical passenger flow data, the extracted EMD components and temporal factors (i.e., the day of the week, the time period of the day, and weekday or weekend) are taken as inputs in the third stage. The experimental results indicate that the proposed hybrid EMD-BPN approach performs well and stably in forecasting the short-term metro passenger flow. © 2011 Elsevier Ltd.
A bayesian dynamic linear model approach for real-time short-term freeway travel time predictionXiang Fei | Chung Cheng Lu | Ke Liu
Volume 19, Issue 6, December 2011, Pages 1306-1318
This paper presents a Bayesian inference-based dynamic linear model (DLM) to predict online short-term travel time on a freeway stretch. The proposed method considers the predicted freeway travel time as the sum of the median of historical travel times, time-varying random variations in travel time, and a model evolution error, where the median is employed to recognize the primary travel time pattern while the variation captures unexpected supply (i.e. capacity) reduction and demand fluctuations. Bayesian forecasting is a learning process that revises sequentially the state of a priori knowledge of travel time based on newly available information. The prediction result is a posterior travel time distribution that can be employed to generate a single-value (typically but not necessarily the mean) travel time as well as a confidence interval representing the uncertainty of travel time prediction. To better track travel time fluctuations during non-recurrent congestion due to unforeseen events (e.g., incidents, accidents, or bad weather), the DLM is integrated into an adaptive control framework that can automatically learn and adjust the system evolution noise level. The experiment results based on the real loop detector data of an I-66 segment in Northern Virginia suggest that the proposed method is able to provide accurate and reliable travel time prediction under both recurrent and non-recurrent traffic conditions. © 2011 Elsevier Ltd.
Understanding individual mobility patterns from urban sensing data: A mobile phone trace exampleFrancesco Calabrese | Mi Diao | Giusy Di Lorenzo | Joseph Ferreira | Carlo Ratti
Volume 26, Issue , January 2013, Pages 301-313
Large-scale urban sensing data such as mobile phone traces are emerging as an important data source for urban modeling. This study represents a first step towards building a methodology whereby mobile phone data can be more usefully applied to transportation research. In this paper, we present techniques to extract useful mobility information from the mobile phone traces of millions of users to investigate individual mobility patterns within a metropolitan area. The mobile-phone-based mobility measures are compared to mobility measures computed using odometer readings from the annual safety inspections of all private vehicles in the region to check the validity of mobile phone data in characterizing individual mobility and to identify the differences between individual mobility and vehicular mobility. The empirical results can help us understand the intra-urban variation of mobility and the non-vehicular component of overall mobility. More importantly, this study suggests that mobile phone trace data represent a reasonable proxy for individual mobility and show enormous potential as an alternative and more frequently updatable data source and a compliment to the conventional travel surveys in mobility study. © 2012 Elsevier Ltd.
Short-term traffic forecasting: Where we are and where we're goingEleni I. Vlahogianni | Matthew G. Karlaftis | John C. Golias
Volume 43, Issue , January 2014, Pages 3-19
Since the early 1980s, short-term traffic forecasting has been an integral part of most Intelligent Transportation Systems (ITS) research and applications; most effort has gone into developing methodologies that can be used to model traffic characteristics and produce anticipated traffic conditions. Existing literature is voluminous, and has largely used single point data from motorways and has employed univariate mathematical models to predict traffic volumes or travel times. Recent developments in technology and the widespread use of powerful computers and mathematical models allow researchers an unprecedented opportunity to expand horizons and direct work in 10 challenging, yet relatively under researched, directions. It is these existing challenges that we review in this paper and offer suggestions for future work. © 2014 Elsevier Ltd.
A decision-making rule for modeling travelers' route choice behavior based on cumulative prospect theoryHongli Xu | Jing Zhou | Wei Xu
Volume 19, Issue 2, April 2011, Pages 218-228
To make practical use of research into travelers' behavior in route choice modeling, a link is required to connect objective travel scenarios with the subjective decisions made by travelers. Cumulative prospect theory (CPT) offers an alternative framework of route choice behavior that goes beyond the conventional expected utility theory (EUT) models. This paper develops a general travel decision-making rule utilizing CPT. It investigates the mechanism of travelers' behavior, examines the probability of applying CPT as a measure of commute utility, and establishes a general utility measurement system, the results of which are found to be more consistent with the experimental data than those of EUT-based route choice models. In addition, an approach to confirm the reference point value is suggested. The main techniques adopted in this study are demonstration analysis, a questionnaire survey, and statistical approaches. © 2010 Elsevier Ltd.
Estimation of a disaggregate multimodal public transport Origin-Destination matrix from passive smartcard data from Santiago, ChileMarcela A. Munizaga | Carolina Palma
Volume 24, Issue , October 2012, Pages 9-18
A high-quality Origin-Destination (OD) matrix is a fundamental prerequisite for any serious transport system analysis. However, it is not always easy to obtain it because OD surveys are expensive and difficult to implement. This is particularly relevant in large cities with congested networks, where detailed zonification and time disaggregation require large sample sizes and complicated survey methods. Therefore, the incorporation of information technology in some public transport systems around the world is an excellent opportunity for passive data collection. In this paper, we present a methodology for estimating a public transport OD matrix from smartcard and GPS data for Santiago, Chile. The proposed method is applied to two 1-week datasets obtained for different time periods. From the data available, we obtain detailed information about the time and position of boarding public transportation and generate an estimation of time and position of alighting for over 80% of the boarding transactions. The results are available at any desired time-space disaggregation. After some post-processing and after incorporating expansion factors to account for unobserved trips, we build public transport OD matrices. © 2012 Elsevier Ltd.
Bi-objective conflict detection and resolution in railway traffic managementFrancesco Corman | Andrea D'Ariano | Dario Pacciarelli | Marco Pranzo
Volume 20, Issue 1, February 2012, Pages 79-94
Railway conflict detection and resolution is the daily task faced by dispatchers and consists of adjusting train schedules whenever disturbances make the timetable infeasible. The main objective pursued by dispatchers in this task is the minimization of train delays, while train operating companies are also interested in other indicators of passenger dissatisfaction. The two objectives are conflicting whenever train delay reduction requires cancellation of some connected services, causing extra waiting times to transferring passengers. In fact, the infrastructure company and the train operating companies discuss on which connection to keep or drop in order to reach a compromise solution.This paper considers the bi-objective problem of minimizing train delays and missed connections in order to provide a set of feasible non-dominated schedules to support this decisional process. We use a detailed alternative graph model to ensure schedule feasibility and develop two heuristic algorithms to compute the Pareto front of non-dominated schedules. Our computational study, based on a complex and densely occupied Dutch railway network, shows that good coordination of connected train services is important to achieve real-time efficiency of railway services since the management of connections may heavily affect train punctuality. The two algorithms approximate accurately the Pareto front in a limited computation time. © 2010 Elsevier Ltd.
An arterial signal optimization model for intersections experiencing queue spillback and lane blockageYue Liu | Gang Len Chang
Volume 19, Issue 1, February 2011, Pages 130-144
This paper presents an arterial signal optimization model that features its effectiveness on: (1) explicitly modeling physical queue evolution on arterial links by lane-group to account for shared-lane traffic interactions; and (2) capturing the dynamic interactions of spillback queues among lane groups and between neighboring intersections due to high demand, geometric constraints, or signal settings. Depending on the detected traffic patterns, one can select the control objective to be either minimizing the total travel time or maximizing the total throughput over the target area. The solution procedures developed with the Genetic Algorithm (GA) have been tested with an example arterial of four intersections under different demand scenarios. Extensive experimental analyses in comparison with results from TRANSYT-7F (version 8) reveal that the proposed model and solution method are quite promising for use in design of arterial signals, especially under congested, high demand traffic conditions. © 2010 Elsevier Ltd.
Lane-based evacuation network optimization: An integrated Lagrangian relaxation and tabu search approachChi Xie | Mark A. Turnquist
Volume 19, Issue 1, February 2011, Pages 40-63
This paper formulates and solves a lane-based evacuation network optimization problem that integrates lane reversal and crossing elimination strategies. To tackle the combinatorial complexity arising from this combined treatment of discrete network capacity and connectivity settings, an integrated Lagrangian relaxation and tabu search solution method is developed. The method takes advantage of Lagrangian relaxation for problem decomposition and complexity reduction while its algorithmic logic is designed based on the principles of tabu search. Numerical results from optimizing a regional evacuation network for a nuclear power plant illustrate the validness and usefulness of the modeling and solution methodology in evacuation planning practice. © 2010 Elsevier Ltd.
Bus stop-skipping scheme with random travel timeZhiyuan Liu | Yadan Yan | Xiaobo Qu | Yong Zhang
Volume 35, Issue , October 2013, Pages 46-56
When a bus is late and behind schedule, the stop-skipping scheme allows the bus vehicle to skip one or more stops to reduce its travel time. The deadheading problem is a special case of the stop-skipping problem, allowing a bus vehicle to skip stops between the dispatching terminal point and a designated stop. At the planning level, the optimal operating plans for these two schemes should be tackled for the benefits of bus operator as well as passengers. This paper aims to propose a methodology for this objective. Thus, three objectives are first proposed to reflect the benefits of bus operator and/or passengers, including minimizing the total waiting time, total in-vehicle travel time and total operating cost. Then, assuming random bus travel time, the stop-skipping is formulated as an optimization model minimizing the weighted sum of the three objectives. The deadheading problem can be formulated via the same minimization model further adding several new constraints. Then, a Genetic Algorithm Incorporating Monte Carlo Simulation is proposed to solve the optimization model. As validated by a numerical example, the proposed algorithm can obtain a satisfactory solution close to the global optimum. © 2013 Elsevier Ltd.
Heterogeneity in car-following behavior: Theory and empiricsSaskia Ossen | Serge P. Hoogendoorn
Volume 19, Issue 2, April 2011, Pages 182-195
The aim of this paper is to gain insights into the level of heterogeneity in car-following behavior in real traffic. We use a large sample of trajectory observations collected by means of a helicopter to identify differences between the car-following behaviors of: (1) passenger car drivers, (2) passenger car drivers and truck drivers and (3) passenger car drivers following a passenger car and passenger car drivers following a truck. We thereto calibrate eight car-following models making different assumptions about the way in which drivers follow their leader(s) on the same lane. We show that considerable behavioral differences exist between passenger car drivers. Different passenger car drivers do not only consider different stimuli (like speed difference(s) with the leading car(s) and distance headway(s) to leading car(s)) but also the extents to which these stimuli influence their behavior differ. Truck drivers turn furthermore out to adopt in general a more robust car-following behavior than passenger car drivers. Their speeds show, for example, less variation over time. We also find indications that the desired headways of passenger car drivers are lower when following a truck than when following a passenger car. © 2010.
The transit network design problem with elastic demand and internalisation of external costs: An application to rail frequency optimisationMariano Gallo | Bruno Montella | Luca D'Acierno
Volume 19, Issue 6, December 2011, Pages 1276-1305
In this paper we examine the transit network design problem under the assumption of elastic demand, focusing on the problem of designing the frequencies of a regional metro. In this problem, investments in transit services have appreciable effects on modal split. Neglecting demand elasticity can lead to solutions that may not represent the actual objectives of the design. We propose four different objective functions that can be adopted to assume demand as elastic, considering the costs of all transportation systems (car, bus and rail) as well as the external costs, and we define the constraints of the problem. Heuristic and meta-heuristic solution algorithms are also proposed. The models and algorithms are tested on a small network and on a real-scale network. © 2011 Elsevier Ltd.
On the assessment of vehicle trajectory data accuracy and application to the Next Generation SIMulation (NGSIM) program dataVincenzo Punzo | Maria Teresa Borzacchiello | Biagio Ciuffo
Volume 19, Issue 6, December 2011, Pages 1243-1262
Trajectories drawn in a common reference system by all the vehicles on a road are the ultimate empirical data to investigate traffic dynamics. The vast amount of such data made freely available by the Next Generation SIMulation (NGSIM) program is therefore opening up new horizons in studying traffic flow theory. Yet the quality of trajectory data and its impact on the reliability of related studies was a vastly underestimated problem in the traffic literature even before the availability of NGSIM data. The absence of established methods to assess data accuracy and even of a common understanding of the problem makes it hard to speak of reproducibility of experiments and objective comparison of results, in particular in a research field where the complexity of human behaviour is an intrinsic challenge to the scientific method. Therefore this paper intends to design quantitative methods to inspect trajectory data. To this aim first the structure of the error on point measurements and its propagation on the space travelled are investigated. Analytical evidence of the bias propagated in the vehicle trajectory functions and a related consistency requirement are given. Literature on estimation/filtering techniques is then reviewed in light of this requirement and a number of error statistics suitable to inspect trajectory data are proposed. The designed methodology, involving jerk analysis, consistency analysis and spectral analysis, is then applied to the complete set of NGSIM databases. © 2011 Elsevier Ltd.
Design of an effective algorithm for fast response to the re-scheduling of railway traffic during disturbancesJohanna Törnquist Krasemann
Volume 20, Issue 1, February 2012, Pages 62-78
An attractive and sustainable railway traffic system is characterized by having a high security, high accessibility, high energy performance and offering reliable services with sufficient punctuality. At the same time, the network is to be utilized to a large extent in a cost-effective way. This requires a continuous balance between maintaining a high utilization and sufficiently high robustness to minimize the sensitivity to disturbances. The occurrence of some disturbances can be prevented to some extent but the occurrence of unpredictable events are unavoidable and their consequences then need to be analyzed, minimized and communicated to the affected users. Valuable information necessary to perform a complete consequence analysis of a disturbance and the re-scheduling is however not always available for the traffic managers. With current conditions, it is also not always possible for the traffic managers to take this information into account since he or she needs to act fast without any decision-support assisting in computing an effective re-scheduling solution. In previous research we have designed an optimization-based approach for re-scheduling which seems promising. However, for certain scenarios it is difficult to find good solutions within seconds. Therefore, we have developed a greedy algorithm which effectively delivers good solutions within the permitted time as a complement to the previous approach. To quickly retrieve a feasible solution the algorithm performs a depth-first search using an evaluation function to prioritise when conflicts arise and then branches according to a set of criteria. © 2010 Elsevier Ltd.
Evacuation transportation modeling: An overview of research, development, and practicePamela Murray-Tuite | Brian Wolshon
Volume 27, Issue , February 2013, Pages 25-45
This paper presents a review of highway-based evacuation modeling and simulation and its evolution over the past decade. The review includes the major components of roadway transportation planning and operations, including the current state of modeling in the forecasting of evacuation travel demand, distribution and assignment of evacuation demand to regional road networks to reach destinations, assignment of evacuees to various modes of transportation, and evaluation and testing of alternative management strategies to increase capacity of evacuation networks or manage demand. Although this discussion does not cover recent work in other modes used in evacuation such as air, rail, and pedestrian, this paper does highlight recent interdisciplinary modeling work in evacuation to help bridge the gap between the behavioral sciences and engineering and the application of emerging techniques for the verification, validation, and calibration of models. The manuscript also calls attention to special considerations and logistical difficulties, which have received limited attention to date. In addition to these concerns, the following future directions are discussed: further interdisciplinary efforts, including incorporating the medical community; using new technologies for communication of warnings and traffic condition information, data collection, and increased modeling resolution and confidence; using real-time information; and further model refinements and validation. © 2012 Elsevier Ltd.