Showing papers in "Transportation Research Part C-emerging Technologies in 2012"

Estimation of a disaggregate multimodal public transport Origin-Destination matrix from passive smartcard data from Santiago, Chile

Abstract: 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

Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks

Abstract: 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.

Design of an effective algorithm for fast response to the re-scheduling of railway traffic during disturbances

Abstract: 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.

Ridership effects of real-time bus information system: A case study in the City of Chicago

Abstract: In this paper, using longitudinal data on route level monthly average weekday ridership in the entire Chicago Transit Authority (CTA) bus system from January 2002 through December 2010, we evaluate the ridership effects of the CTA real-time bus information system. This bus information system is called CTA Bus Tracker and was incrementally implemented on different CTA bus routes from August 2006 to May 2009. To take account of other factors that might affect bus ridership, we also include data on unemployment levels, gas prices, local weather conditions, transit service attributes, and socioeconomic characteristics during the study period. This combined longitudinal data source enables us to implement a quasi-experimental design with statistical controls to examine changes in monthly average weekday ridership, before and after the Bus Tracker system was implemented, on each bus route. Based on a linear mixed model, we found that the provision of Bus Tracker service does increase CTA bus ridership, although the average increase is modest. Further, the study findings suggest that there are temporal variations of the ridership effects among the routes, with the “winning” routes more likely to have the technology implemented in the later phases of the overall “roll-out” period. However, the results are less conclusive regarding geographical variations in the effects of Bus Tracker.

Transit network design: A procedure and an application to a large urban area

Abstract: This paper describes a procedure for solving the bus network design problem and its application in a large urban area (the city of Rome), characterized by: (a) a complex road network topology; (b) a multimodal public transport system (rapid rail transit system, buses and tramways lines); (c) a many-to-many transit demand. The solving procedure consists of a set of heuristics, which includes a first routine for the route generation based on the flow concentration process and a parallel genetic algorithm for finding a sub-optimal set of routes with the associated frequencies. The final goal of the research is to develop an operative tool to support the mobility agency of Rome for the bus network design phase.

Measuring the impact of opening the London shared bicycle scheme to casual users

Abstract: The increasing availability of sensor data in urban areas now offers the opportunity to perform continuous evaluations of transport systems and measure the effects of policy changes, in an empirical, large-scale, and non-invasive way. In this paper, we study one such example: the effect of changing the user-access policy in the London Barclays Cycle Hire scheme. When the scheme was launched in July 2010, users were required to apply for a key to access to the system. By December 2010, this policy was overridden in order to allow for “casual” usage, so that anyone in possession of a debit or credit card could gain access. While the transport authority measured the policy shift’s success by the increased number of trips, we set out to investigate how the change affected the system’s usage throughout the city. We present an extensive analysis of station data collected from the scheme’s web site both pre- and post-policy change, showing how differences in both global and local behaviour can be measured, and how the policy change correlates with a variety of effects observed around the city. We find that, as expected, quicker access to the system correlates with greater week end usage; it also reinforces the week-day commuting trend. In both the pre- and post-change periods, the geographic distribution of activity at individual stations forms concentric circles around central London. However, upon policy change, a number of stations undergo a complete usage change, now exhibiting an opposite trend with respect to that which they had prior to the policy change.

Consistency in multi-vehicle inventory-routing

Abstract: Inventory-routing problems (IRPs) arise in vendor-managed inventory systems. They require jointly solving a vehicle routing problem and an inventory management problem. Whereas the solutions they yield tend to benefit the vendor and customers, solving IRPs solely based on cost considerations may lead to inconveniences to both parties. These are related to the fleet size and vehicle load, to the frequency of the deliveries, and to the quantities delivered. In order to alleviate these problems, we introduce the concept of consistency in IRP solutions, thus increasing quality of service. We formulate the multi-vehicle IRP, with and without consistency requirements, as mixed integer linear programs, and we propose a matheuristic for their solution. This heuristic applies an adaptive large neighborhood search scheme in which some subproblems are solved exactly. The proposed algorithm generates solutions offering a good compromise between cost and quality. We analyze the effect of different inventory policies, routing decisions and delivery sizes.

The retrieval of intra-day trend and its influence on traffic prediction

Abstract: In this paper, we discuss three problems that occur within short-term traffic prediction when the information from only a single point loop detector is used. First, we analyze the retrieval of intra-day trend for traffic flow series and determine whether this retrieval process improves traffic prediction. We compare different highway traffic prediction models that use either the original traffic flow series or the residual time series with the intra-day trend removed. Test results indicate that the prediction performance MAY be significantly improved in the latter scenario. Second, we address two other related questions: the influence of missing data and traffic breakdown prediction. We show that the Probabilistic Principal Component Analysis (PPCA) method, which also utilizes the intra-day trend of traffic flow series, can be a useful tool in imputing the missing data. It can simultaneously ensure that the prediction error remains at an acceptable level, especially when the missing ratio is relatively low. We also show that almost all the known predictors have hidden assumptions of smoothness and, thus, cannot predict the burst points that deviate too far from the intra-day trend. As a result, traffic breakdown points can only be identified but not predicted.

Bi-objective conflict detection and resolution in railway traffic management

Abstract: 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.

Efficient network-wide model-based predictive control for urban traffic networks

Abstract: Traffic congestion has become a stringent issue in urban areas. Traffic control systems are designed to make a better use of the existing traffic infrastructures in order to improve traffic conditions. Along with the fast development of the transportation infrastructures, traffic networks become larger and more complex. Therefore, network-wide traffic control systems that can coordinate the whole network and improve the utilization of the entire traffic infrastructure, are highly required. In this paper, a structured network-wide traffic controller is presented based on Model Predictive Control (MPC) theory. Two macroscopic models are proposed to be the prediction model of the MPC controller. One is more accurate, but correspondingly requires more computation time; the other sacrifices a certain amount of accuracy, but is computationally much more efficient. Based on these two models, MPC controllers are developed. Simulation results show that the MPC controllers are capable of coordinating an urban traffic network, especially in the situations that the traffic flow is not spread evenly through the network. Through reducing the prediction model, the corresponding MPC controller exhibits less on-line computational burden, and thus becomes more applicable in practice. Therefore, it becomes possible for the control system to deal with complex urban road networks more efficiently.

Locating sensors on traffic networks: Models, challenges and research opportunities

Abstract: The problem of optimally locating sensors on a traffic network to measure flows has been object of growing interest in the past few years, due to its relevance in transportation systems. Different locations of sensors on the network can allow, indeed, the collection of data whose usage can be useful for traffic management and control purposes. Many different models have been proposed in the literature as well as corresponding solution approaches. The proposed existing models differ according to different criteria: (i) sensor types to be located on the network (e.g., counting sensors, image sensors, Automatic Vehicle Identification (AVI) readers), (ii) available a-priori information, and (iii) flows of interest (e.g., OD flows, route flows, link flows). The purpose of this paper is to review the existing contributions and to give a unifying picture of these models by categorizing them into two main problems: the Sensor Location Flow-Observability Problem and the Sensor Location Flow-Estimation Problem. For both the problems, we will describe the corresponding computational complexity and the existing results. After describing various models and identifying their advantages and limitations, we conclude with several promising directions for future research and discuss other classes of location problems that address different objectives than the ones reviewed in the paper.

Evaluating passenger robustness in a rail transit network

Abstract: The purpose of this paper is to provide passenger robustness measures for a rail transit network. A network is robust when it reacts well to disruptions on links or stations. In order to measure robustness, indexes relative to the overall travel time of a network when links fail are introduced for two different cases: without-bridging interruptions and with-bridging interruptions. In the first case, passengers either have to wait for the failure to be repaired or find an alternative route in the network, whereas in the second case a bus service between the affected stations is provided and only the failing link is disrupted. A computation of these indexes for the Madrid commuter system shows their applicability.

A schedule-based assignment model with explicit capacity constraints for congested transit networks

Abstract: This paper presents a schedule-based dynamic assignment model for transit networks, which takes into account congestion through explicit vehicle capacity constraints. The core of this assignment model is the use of a joint choice model for departure times, stops and runs that defines a space-time path in which users decide to leave at a given time, to access the network at a given stop and to board a given run to reach their destination. The assignment model is defined through a dynamic process approach in which the within-day network loading procedure allocates users on each transit run according to user choice and to the residual capacity of vehicles arriving at stops. The proposed model, albeit general, is specified for frequent users, who constitute a particularly congestion-sensitive class of users. Finally, an application to a real-size test network (part of the Naples transit network in southern Italy) is illustrated in order to test the proposed approach and show the ability of the modelling framework to assess congestion effects on transit networks.

A new shared vehicle system for urban areas

Abstract: The paper concerns the conceptual design of a transport system for pedestrian areas. The proposed transport system is based on a fleet of eco-sustainable Personal Intelligent City Accessible Vehicles (PICAVs). The vehicles are shared through the day by different users and the following specific services will be provided: instant access, open ended reservation and one way trips. Referring to the proposed transport system, a new methodology to optimise the fleet dimension and its distribution among the stations is proposed in this paper. The problem faced is an optimisation problem where the cost function to be minimised takes into account both the transport system cost and the user costs that depend on the waiting times. A random search algorithm has been adopted. Given a fleet dimension and its distribution among the stations, the waiting times of the users are assessed by a microscopic simulation. The simulation model tracks the second-by-second activity of each PICAV user, as well as the second-by-second activity of each vehicle. The overall methodology has been implemented in an object-oriented simulator. The proposed transport system has been planned and simulated for the historical city centre of Genoa, Italy.

Transit route network design-maximizing direct and transfer demand density

Abstract: Transit network design is an important part of urban transportation planning. The purpose of this paper is to build on direct traveler density model and extend it to design transit network considering demand density relating to direct demands and transfers, and lengths of routes. The proposed method aiming to maximize demand density of route under some resource constraints divides transit network design problem into three stages, i.e., skeleton route design, main route design and branch route design, based on the objective functions with different transfer coefficients. An ant colony optimization (ACO) is used to solve the model. The model and algorithm are illustrated with data from Dalian city, China and results show that the approach can improve the solution quality if the transfer coefficient is reasonably set.

Applicability of highway alignment optimization models

Abstract: This paper presents an intelligent optimization tool that assists planners and designers in finding preferable highway alignments, connecting specified endpoints or zones. It integrates genetic algorithms with a geographic information system (GIS) for optimizing highway alignments and processes massive amounts of relevant data associated with highway design and alternative evaluation. To show the applicability of the proposed model to a real-world problem, two actual highway projects in the state of Maryland have been analyzed using the model. An extensive analysis of sensitivity to key model parameters is also conducted to describe the model capabilities. The analysis results show that the model can effectively optimize highway alignments in an area combining complex terrain and various types of natural and cultural land-use patterns, and provide detailed information of optimized alignments as a model output. It is also found that the alignments optimized by the model are quite similar to those obtained through conventional manual methods by a state agency, but the model can greatly reduce the time required for highway planning and design as well as produce lower cost solutions. Finally, the results confirm that all dominating and alignment-sensitive costs should be simultaneously evaluated in the alignment optimization process because many trade-off opportunities exist among those costs. The proposed model can greatly contribute to the productivity of highway planners as well as to the quality of the resulting infrastructure.

A dynamic traffic assignment model for highly congested urban networks

Abstract: The management of severe congestion in complex urban networks calls for dynamic traffic assignment (DTA) models that can replicate real traffic situations with long queues and spillbacks. DynaMIT-P, a mesoscopic traffic simulation system, was enhanced and calibrated to capture the traffic characteristics in a sub-area of Beijing, China. The network had 1698 nodes and 3180 directed links in an area of around 18 square miles. There were 2927 non-zero origin–destination (OD) pairs and around 630,000 vehicles were simulated over 4 h of the morning peak. All demand and supply parameters were calibrated simultaneously using sensor counts and floating car travel time data. Successful calibration was achieved with the Path-size Logit route choice model, which accounted for overlapping routes. Furthermore, explicit representations of lane groups were required to properly model traffic delays and queues. A modified treatment of acceptance capacity was required to model the large number of short links in the transportation network (close to the length of one vehicle). In addition, even though bicycles and pedestrians were not explicitly modeled, their impacts on auto traffic were captured by dynamic road segment capacities.

Robust optimization model of schedule design for a fixed bus route

Abstract: The time control point strategy is often adopted by bus operators in China and Singapore to provide more reliable transit service. It is thus important to design a schedule, in which bus drivers should devote their efforts to catch up a scheduled arrival time at a predetermined time control point on a bus route because passengers can definitely benefit from a reliable bus route schedule. This paper first proposes a novel reliable bus route schedule design problem by taking into account the bus travel time uncertainty and the bus drivers’ schedule recovery efforts. It proceeds to develop a robust optimization model for the proposed problem, which aims to minimize the sum of the expected value of the random schedule deviation and its variability multiplied by a weighting value. A Monte Carlo simulation based solution method is subsequently designed to solve the robust optimization model. Finally, a numerical example based on a real bus route in Suzhou city of China is carried out to demonstrate the strength of the robust optimization model. We find that the optimal scheduled travel time (or slack time) depends on bus drivers’ schedule recovery behavior and on decision makers’ scheduling philosophies.

Real-time prediction of visibility related crashes

Abstract: More researchers started using real-time traffic surveillance data, collected from loop/radar detectors (LDs), for proactive crash risk assessment. However, there is a lack of prior studies that investigated the link between real-time traffic data and crash risk of reduced visibility related (VR) crashes. Two issues that have not explicitly been addressed in prior studies are; (1) the possibility of predicting VR crashes using traffic data collected from the Automatic Vehicle Identification (AVI) sensors installed on Expressways and (2) which traffic data are advantageous for predicting VR crashes; LDs or AVIs. Thus, this study attempts to examine the relationships between VR crash risk and real-time traffic data collected from LDs installed on two Freeways in Central Florida (I-4 and I-95) and from AVI sensors installed on two Expressways (SR 408 and SR 417). Also, it investigates which data are better for predicting VR crashes. The approach adopted here involves developing Bayesian matched case-control logistic regression models using the historical crashes, LDs and AVI data. Regarding the model estimated based on LDs data, the average speed observed at the nearest downstream station along with the coefficient of variation in speed observed at the nearest upstream station, all at 5-10 min prior to the crash time, were found to have significant effect on VR crash risk. However, for the model developed based on AVI data, the coefficient of variation in speed observed at the crash segment, at 5-10 min prior to the crash time, affected the likelihood of VR crash occurrence. The results showed that both LDs and AVI systems can be used for safety application (i.e., predicting VR crashes). It was found that up to 73% of VR crashes could be identified correctly. Argument concerning which traffic data (LDs or AVI) are better for predicting VR crashes is also provided and discussed.

Towards a driver fatigue test based on the saccadic main sequence: A partial validation by subjective report data

Abstract: Developing a valid measurement of mental fatigue remains a big challenge and would be beneficial for various application areas, such as the improvement of road traffic safety. In the present study we examined influences of mental fatigue on the dynamics of saccadic eye movements. Based on previous findings, we propose that among amplitude and duration of saccades, the peak velocity of saccadic eye movements is particularly sensitive to changes in mental fatigue. Ten participants completed a fixation task before and after 2 h of driving in a virtual simulation environment as well as after a rest break of fifteen minutes. Driving and rest break were assumed to directly influence the level of mental fatigue and were evaluated using subjective ratings and eye movement indices. According to the subjective ratings, mental fatigue was highest after driving but decreased after the rest break. The peak velocity of saccadic eye movements decreased after driving while the duration of saccades increased, but no effects of the rest break were observed in the saccade parameters. We conclude that saccadic eye movement parameters—particularly the peak velocity—are sensitive indicators for mental fatigue. According to these findings, the peak velocity analysis represents a valid on-line measure for the detection of mental fatigue, providing the basis for the development of new vigilance screening tools to prevent accidents in several application domains.

Schedule-driven intersection control

Abstract: Model-based intersection optimization strategies have been widely investigated for distributed traffic signal control in road networks Due to the form of “black-box” optimization that is typically assumed, a basic challenge faced by these strategies is the combinatorial nature of the problem that must be solved The underlying state space is exponential in the number of time steps in the look-ahead optimization horizon at a given time resolution In this paper, we present a schedule-driven intersection control strategy, called SchIC, which addresses this challenge by exploiting the structural information in non-uniformly distributed traffic flow Central to our method is an alternative formulation of intersection control optimization as a scheduling problem, which effectively reduces the state space through use of an aggregate representation on traffic flow data in the prediction horizon A forward recursive algorithm is proposed for solving the scheduling problem, which makes use of a dominance condition to efficiently eliminate most states at early stages SchIC thus achieves near optimal solutions with a polynomial complexity in the prediction horizon, and is insensitive to the granularity of time resolution that is assumed The performance of SchIC with respect to both intersection control and implicit coordination between intersections is evaluated empirically on two ideal scenarios and a real-world urban traffic network Some characteristics and possible real-world extensions of SchIC are also discussed

Development of map matching algorithm for low frequency probe data

Abstract: In order to lower the operating costs of a large scale probe vehicle system, countermeasures for decreasing operating cost of the system, such as lowering of data polling frequency and use an existing fleet management system, are necessary. Such countermeasures, however, reduce the accuracy of the traffic information that is generated from the collected probe vehicle data. In this study, the authors developed several map matching algorithms that can be applied to low frequency and little information probe vehicle data. These map matching algorithms were verified using actual probe vehicle data collected in the area around Nagoya, Japan. The results show that the data can be map matched with a high degree of accuracy by combining an appropriate link cost, generation of reasonable candidate routes, evaluation of the routes, and introducing the concept of a driver’s route choice.

Control of metro-trains equipped with onboard supercapacitors for energy saving and reduction of power peak demand

Abstract: New generation of rapid transit trains requires a more effective energy management for the reduction of energy consumption during the journey. Rapid transit trains can benefit substantially form aboard electric storage devices for the recuperation of the kinetic energy during braking and the limitation of power supplier current during acceleration. This paper proposes a control strategy for aboard supercapacitors integrated with motor drive control. The voltage and current references for supercapacitors are related to the actual train speed and calculated on the basis of train inertial forces and supercapacitors state of charge. The proposed control strategy is very useful for obtaining good performances also with not predefined speed cycles. Therefore, the control strategy has been verified on a generic traction cycle via numerical simulations and experimental tests, made on an expressly built electromechanical simulator. The results obtained point out that the proposed control is capable of achieving energy saving and reducing considerably the voltage surge at the overhead contact line during train braking.

An energy optimization strategy for power-split drivetrain plug-in hybrid electric vehicles

Abstract: To demonstrate the greater capabilities and benefits achievable with a plug-in hybrid electric vehicle (PHEV), an energy optimization strategy for a power-split drivetrain PHEV, which utilizes a predicted speed profile, is presented. In addition, the paper reports an analysis and evaluation of issues related to real time control implementation for the modeled PHEV system, which include the optimization window sizes and the impact of prediction errors on the energy optimization strategy performance. The optimization time window sizes were identified and validated for different driving cycles under different operating modes and total length of travel. With the identified optimization windows size, improvements in fuel consumption were realized; the highest improvement was for Urban Dynamometer Driving Schedule (UDDS), with a range of improvement of 14–31%, followed by a 1–15% range of improvement for Highway Fuel Economy Driving Schedule (known as HWFET) and a 1–8% range of improvement for US06 (also known as Supplemental Federal Test Procedure). While no correlation was observed between the error rate and the rate of increased fuel consumption, this PHEV system still yielded energy savings with errors in the speed prediction, which is an indication of robustness of this PHEV model.

Dealing with the efficient allocation of scarce resources at congested airports

Abstract: The existing slot allocation mechanism, based on the International Air Transport Association (IATA) system and its complementary version of the European Union (EU) regulation, produces rather poor capacity allocation outcomes for congested EU airports since it fails to properly match slots requested with slots allocated to airlines. Inefficiencies during the initial allocation are mainly due to the problem complexity in conjunction to limited decision support available to slot coordinators. On the other hand, substantial inefficiencies give rise to severe slot misuse and unreasonably low utilisation of airport resources running already into scarcity. The objective of this paper is to develop an optimisation-based model implementing the existing EU/IATA rules, operational constraints, and coordination procedures with the ultimate objective to better accommodate airlines’ preferences at coordinated airports through the minimisation of the difference between the requested and the allocated slot times to airlines. The results of the model are assessed and compared vis-a-vis the allocation outcome produced according to current slot coordination practice in three regional Greek airports. The proposed model produces very promising results and demonstrates that there is large room for improvement of the efficiency of the current allocation outcome in a range between 14% and 95%. The discussion of the model results is complemented by a sensitivity analysis highlighting the importance of declared capacity and the magnitude of its influence on slot allocation efficiency.

Stationary ultracapacitors storage device for improving energy saving and voltage profile of light transportation networks

Abstract: The installation of stationary ultracapacitor storage devices, as widely recognized, allows the recovery of the braking energy for increasing the energy efficiency as well as a better pantograph voltage profile. In the paper a new methodological mean is proposed for determining the fundamental characteristics of this kind of storage device, characterized by high power density, interfaced with the railroad by a bidirectional dc–dc converter. More specifically, the parameters of the storage system can be determined by employing an optimization technique which in a quite general way is able to take contemporaneously into account several aspects in an integrated manner. Some considerations are performed for properly taking into account the stochastic aspects of the design procedure. Numerical simulations with respect to a case study are presented, pointing out the potentiality of the tailored technique. Experimental results are also reported, with reference to an electromechanical simulator, in order to put in evidence the effectiveness and the actual implementation of the proposed optimization technique.

Selective data collection in vehicular networks for traffic control applications

Abstract: Vehicular sensor network (VSN) is an emerging technology, which combines wireless communication offered by vehicular ad hoc networks (VANETs) with sensing devices installed in vehicles. VSN creates a huge opportunity to extend the road-side sensor infrastructure of existing traffic control systems. The efficient use of the wireless communication medium is one of the basic issues in VSN applications development. This paper introduces a method of selective data collection for traffic control applications, which provides a significant reduction in data amounts transmitted through VSN. The underlying idea is to detect the necessity of data transfers on the basis of uncertainty determination of the traffic control decisions. According to the proposed approach, sensor data are transmitted from vehicles to the control node only at selected time moments. Data collected in VSN are processed using on-line traffic simulation technique, which enables traffic flow prediction, performance evaluation of control actions and uncertainty estimation. If precision of the resulting information is insufficient, the optimal control action cannot be derived without ambiguity. As a result the control decision becomes uncertain and it is a signal informing that new traffic data from VSN are necessary to provide more precise prediction and to reduce the uncertainty of decision. The proposed method can be applied in traffic control systems of different types e.g. traffic signals, variable speed limits, and dynamic route guidance. The effectiveness of this method is illustrated in an experimental study on traffic control at signalised intersection.

An aircraft boarding model accounting for passengers' individual properties

Abstract: Aircraft boarding is a very complex process. In this paper, we propose a new aircraft boarding model with consideration of passengers’ individual properties. The model is then applied to explore the dynamic properties of passengers’ motions under three different aircraft boarding strategies including the random boarding strategy, the boarding strategy based on passenger’s seat serial number and individual properties. Our numerical results illustrate that overtaking, queue-jumping, seat conflict congestions and jams may occur under the first two boarding strategies, but these phenomena do not occur under the third boarding strategy. The results indicate that the third boarding strategy is more effective than the other two boarding strategies.

Evaluation of the effect of cooperative infrastructure-to-vehicle systems on driver behavior

Abstract: In-vehicle technologies and co-operative services have potential to ease congestion problems and improve traffic safety. This paper investigates the impact of infrastructure-to-vehicle co-operative systems, case of CO-OPerative SystEms for Intelligent Road Safety (COOPERS), on driver behavior. Thirty-five test drivers drove an instrumented vehicle, twice, with and without the system. Data related to driving behavior, physiological measurements, and user acceptance was collected. A macro-level approach was used to evaluate the potential impact of such systems on driver behavior and traffic safety. The results in terms of speeds, following gaps, and physiological measurements indicate a positive impact. Furthermore, drivers' opinions show that the system is in general acceptable and useful.

An analysis of the factors influencing differences in survey-reported and GPS-recorded trips

Abstract: Recent advances in global positioning systems (GPS) technology have resulted in a transition in household travel survey methods to test the use of GPS units to record travel details, followed by the application of an algorithm to both identify trips and impute trip purpose, typically supplemented with some level of respondent confirmation via prompted-recall surveys. As the research community evaluates this new approach to potentially replace the traditional survey-reported collection method, it is important to consider how well the GPS-recorded and algorithm-imputed details capture trip details and whether the traditional survey-reported collection method may be preferred with regards to some types of travel. This paper considers two measures of travel intensity (survey-reported and GPS-recorded) for two trip purposes (work and non-work) as dependent variables in a joint ordered response model. The empirical analysis uses a sample from the full-study of the 2009 Indianapolis regional household travel survey. Individuals in this sample provided diary details about their travel survey day as well as carried wearable GPS units for the same 24-h period. The empirical results provide important insights regarding differences in measures of travel intensities related to the two different data collection modes (diary and GPS). The results suggest that more research is needed in the development of workplace identification algorithms, that GPS should continue to be used alongside rather than in lieu of the traditional diary approach, and that assignment of individuals to the GPS or diary survey approach should consider demographics and other characteristics.