We describe the design and construction of a headlight glare simulator to be used with a driving simulator. The system combines a modified programmable off-the-shelf LED display board and a beamsplitter so that the LED lights, representing the headlights of oncoming cars, are superimposed over the driving simulator headlights image. Ideal spatial arrangement of optical components to avoid misalignments of the superimposed images is hard to achieve in practice and variations inevitably introduce some parallax. Furthermore, the driver's viewing position varies with driver's height and seating position preferences exacerbate such misalignment. We reduce the parallax errors using an intuitive calibration procedure (simple drag-and-drop alignment of nine LED positions with calibration dots on the screen). To simulate the dynamics of headlight brightness changes when two vehicles are approaching, LED intensity control algorithms based on both headlight and LED beam shapes were developed. The simulation errors were estimated and compared to real-world headlight brightness variability.
This paper deals with the traffic flow stability/instability
induced by a particular adaptive cruise control policy, known as the
“constant time headway policy”. The control policy is
analyzed for a circular highway using three different traffic models,
namely a microscopic model, a spatially discrete model, and a spatially
continuous model. It is shown that the traffic dynamics will not be
qualitatively consistent across the three modeling paradigms if a
consistent biasing strategy is not used to adapt the constant time
headway policy. The biasing strategy determines whether the feedback
quantity for use in the control is taken colocatedly, downstream or
upstream to the vehicle section/highway location. For ACC vehicles
equipped with forward looking sensors, the downstream biasing strategy
should be used. In this case, the constant time headway policy induces
exponentially stable traffic flow on a circular highway in all three
Due to the many complex aspects of a traffic system, it has been difficult to determine the optimal signal timing. Much of this difficulty has stemmed from the need to build extremely complex models of the traffic dynamics as a component of the control strategy. This paper presents a fundamentally different approach for optimal signal timing that eliminates the need for such complex models. The approach is based on a neural network serving as the basis for the control law, with the weight estimation occurring in closed-loop mode via the simultaneous perturbation stochastic approximation (SPSA) algorithm. Since the SPSA algorithm requires only loss function measurements, there is no system-wide model required for the weight estimation
This paper discusses the optimal coordination of variable speed limits and ramp metering in a freeway traffic network, where the objective of the control is to minimize the total time that vehicles spend in the network. Coordinated freeway traffic control is a new development where the control problem is to find the combination of control measures that results in the best network performance. This problem is solved by model predictive control, where the macroscopic traffic flow model METANET is used as the prediction model. We extend this model with a model for dynamic speed limits and for main-stream origins. This approach results in a predictive coordinated control approach where variable speed limits can prevent a traffic breakdown and maintain a higher outflow even when ramp metering is unable to prevent congestion (e.g., because of an on-ramp queue constraint). The use of dynamic speed limits significantly reduces congestion and results in a lower total time spent.Since the primary effect of the speed limits is the limitation of the main-stream flow, a comparison is made with the case where the speed limits are replaced by main-stream metering. The resulting performances are comparable. Since the range of flows that main-stream metering and dynamic speed limits can control is different, the choice between the two should be primarily based on the traffic demands.
The personal electric vehicle (PEV) emerged as a new category of transportation device in the late 1990s. PEVs transport a single passenger over trip distances of 1–10 km and employ electricity as the motive energy source. The category is principally comprised of electric-powered scooters and cycles. Personal electric vehicles offer several potential benefits to consumers and to society including lower transportation costs, reduced trip times, and lower environmental impact. The PEV therefore offers many intriguing possibilities for extending the human range of mobility from about 1 km (via walking) to 10 km or more. However, the full potential of the category has not been realized, to a large extent because the vehicles are not yet light enough, do not go far enough, and cost too much. The main question addressed by this article is what are the technological limits on personal electric vehicle design? And more specifically, How light can PEVs be? How far can they go? How little can they cost? What are the trade-offs across these dimensions of performance at the efficient frontier? The methodological approach of the paper is to combine a technology assessment of the major subsystems of a PEV with a technical model of vehicle performance in order to estimate the cost and mass of a vehicle for a given set of functional requirements.
Advances in wireless communications are facilitating the development of inter-vehicle communication systems that will benefit mobility and safety objectives. Recently, these systems, referred as vehicular ad hoc networks (VANETs), are gaining significant prominence from both government agencies and private organizations. VANETs are characterized by high vehicle mobility, unexpected driver behavior and variable traffic environment which bring forth challenges to maintain good connectivity. This study considers VANETs as a nominal system with disturbance. Under the nominal system, the traffic space headway is assumed to follow an approved traffic flow distributions, such as exponential distribution. Disturbance is then used to capture a set of uncertain traffic flow events caused by driver behavior and changes in traffic flow. In addition, robustness factor is incorporated to present the impact of probabilistic disturbance events that disrupt the node connectivity. Under constant disturbance conditions, the lower bound of reachable neighbors for each vehicle to maintain a high connectivity is analytically derived. Furthermore, we obtain the relationship between the number of nodes in a VANET and the reachable neighbors under which the network is asymptotically connected. Finally, in variable disturbance situations, the interaction between robustness factor and macroscopic traffic parameters are investigated based on the simulation data. The validation results demonstrate that the proposed analytical characterization can approximate VANET connectivity very well. Our results facilitate the understanding of VANET connectivity on a freeway segment under different traffic conditions.
Hazardous materials (hazmat) are potentially harmful to people and environment due to their toxic ingredients. Although a significant portion of hazmat is transported via railroads, prevailing studies on dangerous goods focus on highway shipments. In this work, we develop a risk assessment methodology that takes into consideration the differentiating features of trains and the characteristics of train accident. The proposed approach incorporates train-length, train-decile position of hazmat railcar, the sequence of events leading to hazmat release, and the associated consequence from ruptured railcars. Freight-train derailment reports of the Federal Railroad Administration were analyzed to both get a better understanding of the problem, and also to propose a more precise assessment methodology. The assessment methodology, which includes Bayes Theorem and Logical Diagrams, were used to study a US based case example, which was further analyzed to gain relevant managerial insights.
Within the transportation research literature, the attempt to understand and predict the level of car ownership is probably one of the most popular areas of study. The primary reason for this is understandable, having access to a vehicle increases an individual’s (or their household’s) travel options, leading to greater mobility. Secondary reasons for this scrutiny include the need to predict future transport investment in road infrastructure and the commercial demand for new vehicles. This paper attempts to predict the level of household car ownership as a function of the characteristics of the household and the individuals that make up the household. The primary data source for this study comes from the 2001 United Kingdom Census and the analysis methods used are from the discipline of data mining. The results of this study are in line with those from previous research but show a potential to predict the higher levels of household car ownership with greater accuracy than other similar studies.
A practical system is described for the real-time estimation of travel time across an arterial segment with multiple intersections. The system relies on matching vehicle signatures from wireless sensors. The sensors provide a noisy magnetic signature of a vehicle and the precise time when it crosses the sensors. A match (re-identification) of signatures at two locations gives the corresponding travel time of the vehicle. The travel times for all matched vehicles yield the travel time distribution. Matching results can be processed to provide other important arterial performance measures including capacity, volume/capacity ratio, queue lengths, and number of vehicles in the link. The matching algorithm is based on a statistical model of the signatures. The statistical model itself is estimated from the data, and does not require measurement of ‘ground truth’. The procedure does not require measurements of signal settings; in fact, signal settings can be inferred from the matched vehicle results. The procedure is tested on a 1.5 km (0.9 mile)-long segment of San Pablo Avenue in Albany, CA, under different traffic conditions. The segment is divided into three links: one link spans four intersections, and two links each span one intersection.
Perishable foods are frequently exposed to temperature abuse during transportation and distribution. The use of traditional data loggers do not permit the instantaneous data transmission that radio frequency technology offers. Temperature has a major impact on food quality and safety, particularly when long transit times are imposed. Consequently, using radio frequency identification (RFID) to track and monitor temperature in perishable shipments will bring significant benefits to the cold chain. The goal of this study was to determine the optimal RF antenna placement to achieve full RFID tag readability inside a sea container. Testing was made at two different frequencies (915 and 433 MHz) while the refrigeration unit was running at −25 °C and the container was fully loaded with frozen bread. The sea container was instrumented with eight RFID antennas, three of which were tuned for 433 MHz and five for 915 MHz. All antenna wires exited the container via the forward drain holes. The RFID readers were outside the container and connected to their respective antennas, one at a time. Thirty eight RFID tags were evenly distributed onto the pallets of frozen bread. All RFID tags were active tags capable of reading and recording temperature. Results at 915 MHz showed readability levels between 47% and 79%, with an average of 68.4%, whereas 433 MHz demonstrated 100% readability at all antenna positions. In conclusion, the 433 MHz RFID system appears suitable for real time temperature monitoring of frozen bread inside a sea container. This technology could be applied to other food items similar to frozen bread.Highlights► We tested RFID antenna position for temperature monitoring in sea transportation. ► We tested two frequencies on a full load of frozen bread. ► The results showed that antenna position affects RFID readability at 915 MHz. ► No deviation was observed within the readings made at 433 MHz.
This paper presents the design, implementation, and partial evaluation work performed by a European consortium for the development of a Variable Message Sign (VMS) information and guidance system in the city of Aalborg, Denmark. The employed control strategy is based on simple automatic control concepts with decentralized feedback loops aiming at approximating a user optimal traffic flow distribution in the mixed network, that comprises a motorway axis and an urban component. Simulation studies demonstrate the potential improvements achievable with this kind of control measures and control strategies. The implementation concept and first field results are outlined.
The Braess Paradox is a well-known phenomenon: adding a new road to a traffic network may not reduce the total travel time. In fact, some road users may be better off but they contribute to an increase in travel time for other users. This situation happens because drivers do not face the true social cost of an action. Some measures have been proposed to at least minimize the effects of the paradox. However, it is not realistic to assume that the drivers would have all the necessary knowledge in order to compute their rewards from a point-of-view which is not their own, i.e. it cannot be expected that drivers would consider the global performance of the system. Therefore this paper discusses the effects of giving route recommendation to drivers in order to divert them to a situation in which the effects of the paradox are reduced. Two contributions are presented: a generalized cost function for the abstract model, which is valid for any number of drivers, and the calibration and results for a microscopic simulation, where the cost functions are not necessary anymore. These are replaced in the microscopic simulation by the real commuting time perceived by each driver. In all cases we use a learning mechanism to allow drivers to adapt to the changes in the environment. Different rates of drivers receive route recommendation with different rates of acceptance. We show that it is useful to manipulate the route information given to the agents.
Modern decision support systems (DSS) not only store large amounts of decision-relevant data, but also aim at assisting decision-makers to explore the meaning of that data, and to take decisions based on understanding. In transportation domains, a multiagent approach to the construction of DSS is becoming increasingly popular, because it does not only reduce design complexity, but it also adequately supports a dialogue-based stance on decision support interactions. However, despite recent advances in the field of agent-oriented software engineering, a principled approach to the design of multiagent systems for decision support is still to come.In this paper, we outline a design method for the construction of agent-based DSS. Setting out from an organisational and communicative model of decision support environments, we present an abstract architecture for multiagent DSS. We then show how this architecture is instantiated for real-world problems by means of two prototypes for transportation management.
There is no standard way of measuring driver acceptance of new technology. A review of the literature shows that there are almost as many methods of assessment of acceptance as there are acceptance studies. The tool for studying acceptance of new technological equipment that is presented here has a major advantage compared with many other studies in that esoteric knowledge of scaling techniques is not required. The technique is simple and consists of nine 5-point rating-scale items. These items load on two scales, a scale denoting the usefulness of the system, and a scale designating satisfaction. The technique has been applied in six different studies in different test environments and analyses performed over these studies show that it is a reliable instrument for the assessment of acceptance of new technology. The technique was sensitive to differences in opinion to specific aspects of in-vehicle systems, as well as to differences in opinion between driver groups. In a concluding section explicit recommendations for use of the scale are given.
In order to reduce the number of vehicles stuck in congestion, especially for stop-and-go traffic at toll plazas, the establishment of electronic toll collection (ETC) systems has been a hot issue and dominant trend in many countries. Taiwan has joined the crowd, adding an ETC system to its toll roads in early 2006. However, despite the potential benefits for motorists, the utilization rate has been lower than expected during the introductory stage. The objective of this study is to advance our understanding on the critical antecedents of motorists’ intention of ETC service adoption by integrating both technology acceptance model (TAM) and theory of planned behavior (TPB) perspectives. Through empirical data collection and analysis from highway motorists who had not installed on-board units (OBU) for ETC service in Taiwan, we found that system attributes, perceived usefulness and perceived ease of use, indeed, positively engender motorists’ attitudes towards ETC service adoption. Moreover, results also reveal that attitude, subjective norm and perceived behavioral control positively influence the intention of ETC system adoption. Implications for practitioners and researchers, and suggestions for future research are also addressed in this study.
Intelligent Transportation System (ITS) can play an important role in reducing risks and increasing traffic safety. Discussion as to whether a technological approach or a behavioral approach is the right way to achieve a safer traffic environment forms a point of departure for this paper. On the one hand, there are the technicians who emphasize technology as the way towards safer traffic. Behaviorists, on the other hand, view the drivers’ behavior as fundamental and argue that education and incentive-oriented policies are essential in order to influence the driver and therefore increase traffic safety. Independent of the approach advocated a successful outcome of either a technological improvement, or an information campaign, has to be based on a high level of acceptance among potential users. In order to increase traffic safety, it is therefore essential to recognize driver motivation and attitudes. In this paper we focus on drivers’ attitudes towards risk, traffic safety and safety measures. A study of drivers’ attitudes and acceptance of an electronic device for speed checking (which the drivers tested for nine months) indicated a high acceptance level. The drivers perceived that they had both become more aware of traffic regulations and behaved in accordance with safety regulations.
We were contracted to test a suite of proposed location messaging standards for the intelligent transportation systems (ITS) industry. We studied six different databases for the County of Santa Barbara, documented types and magnitudes of error, and examined the likely success of the proposed standards. This paper synthesizes the test results and identifies caveats for the user community as well as challenges to academia. We conclude that, first, current messaging proposals are inadequate, and superior methods are required to convey both location and a measure of confidence to the recipient. Second, there is a need to develop methods to correct map data geometrically, so that location is more accurately captured, stored and communicated, particularly in mission critical applications such as emergency servicing. To address this, we have developed methods for comparing maps and adjusting them in real time. Third, there must be standards for centerline map accuracy, that reflect the data models and functions associated with transportation.
Taxi-out delay is a significant portion of the block time of a flight. Uncertainty in taxi-out times reduces predictability of arrival times at the destination. This in turn results in inefficient use of airline resources such as aircraft, crew, and ground personnel. Taxi-out time prediction is also a first step in enabling schedule modifications that would help mitigate congestion and reduce emissions. The dynamically changing operation at the airport makes it difficult to accurately predict taxi-out time. In this paper we investigate the accuracy of taxi out time prediction using a nonparametric reinforcement learning (RL) based method, set in the probabilistic framework of stochastic dynamic programming. A case-study of Tampa International Airport (TPA) shows that on an average, with 93.7% probability, on any given day, our predicted mean taxi-out time for any given quarter, matches the actual mean taxi-out time for the same quarter with a standard error of 1.5 min. Also, for individual flights, the taxi-out time of 81% of them were predicted accurately within a standard error of 2 min. The predictions were done 15 min before gate departure. Gate OUT, wheels OFF, wheels ON, and gate IN (OOOI) data available in the Aviation System Performance Metric (ASPM) database maintained by the Federal Aviation Administration (FAA) was used to model and analyze the problem. The prediction accuracy is high even without the use of detailed track data.
Digital maps can provide support for numerous advanced driver assistance systems (ADAS) aimed at improving road safety. These new uses require more highly detailed and precise maps. The use of a datalog vehicle to collect roadway geometry data can fulfil these specifications. This paper presents fast, accurate measurement with an on-board inertial system together with a method to evaluate measurement uncertainty, particularly for any variables obtained indirectly. It also presents an algorithm for segmentation and fitting geometric curves to the experimental points, following current highway design standards. The algorithms have been applied to real road measurements. Segmentation has been done in straight alignments, circular curves and transition curves whose characteristic parameters are calculated. It has been seen that with a very small data set it is possible to reconstruct the measured geometry with few discrepancies regarding the experimental points.
The aim of this research is to investigate the feasibility of developing a traffic monitoring detector for the purpose of reliable on-line vehicle classification to aid traffic management systems. The detector used was a directional microphone connected to a DAT (Digital Audio Tape) recorder. The digital signal was pre-processed by LPC (Linear Predictive Coding) parameter conversion based on autocorrelation analysis. A Time Delay Neural Network (TDNN) was chosen to classify individual travelling vehicles based on their speed-independent acoustic signature. Locations for data acquisition included roadside recordings at a number of two-way urban road sites in the city of Leeds with no control over the environmental parameters such as background noise, interference from other travelling vehicles or the speed of the recorded vehicles. The results and performance analysis of TDNN vehicle classification, the convergence for training patterns and accuracy of test patterns are fully illustrated. The paper also provides a description of the TDNN architecture and training algorithm, and an overview of the LPC pre-processing and feature extraction technique as applied to audio monitoring of road traffic. In the final phase of the experiment, the four broad categorisations of vehicles for training the network consisted of: buses or lorries; small or large saloons; various types of motorcycles; and light goods vehicles or vans. A TDNN network was successfully trained with 94% accuracy for the training patterns and 82.4% accuracy for the test patterns.
Current day condition monitoring applications involving wood are mostly carried out through visual inspection and if necessary some impact acoustic examination is carried out. These inspections are mainly done intuitively by skilled personnel. In this paper, a pattern recognition approach has been considered to automate such intuitive human skills for the development of robust and reliable methods within the area. The study presents a comparison of several pattern recognition techniques combined with various stationary feature extraction techniques for classification of impact acoustic emissions. Further issues concerning feature fusion are discussed as well. It is hoped that this kind of broad analysis could be used to handle a wide spectrum of tasks within the area, and would provide a perfect ground for future research directions. A brief introduction to the techniques is provided for the benefit of the readers unfamiliar with the techniques.Pattern classifiers such as support vector machines, etc. are combined with stationary feature extraction techniques such as linear predictive cepstral coefficients, etc. Results from support vector machines in combination with linear predictive cepstral coefficients delivered good classification rates. However, Gaussian mixture models delivered higher classification rates when feature fusion is proposed.
This study aims at analyzing drivers' behavior in acquiring and using traffic information in an environment with multiple information sources. Accordingly, information acquisition and reference models are developed in an effort to show the empirical relationship between drivers' reaction to multiple information sources, causal factors latent psychological ones, traffic conditions at the time of traveling and the accuracy of traffic information available. A route choice model is proposed that takes into account the information acquisition and reference process. Model validity is investigated using data collected on the Tokyo Metropolitan Expressway, which has four different types of information sources.
Conventional vehicle detectors are capable of monitoring discrete points along the freeway but do not provide information about conditions on the link between detectors. Knowledge of conditions on the link is useful to operating agencies for enabling timely decisions in response to various delay causing events and hence to reduce the resulting congestion of the freeway system. This paper presents an approach that matches vehicle measurements between detector stations to provide information on the conditions over the link between the detectors rather than relying strictly on the aggregate point measurements from the detectors. In particular this work reidentifies measurements from distinct vehicles using the existing loop detector infrastructure. Here the distinct vehicles are the long vehicles, but depending on the vehicle population or type of detector used, one might chose to use some other reproducible feature.This new methodology represents an important advancement over preceding loop based vehicle reidentification, as illustrated herein, it enables vehicle reidentification across a major diverge and a major merge. The examples include a case where the reidentification algorithm responded to delay between two detector stations an hour before the delay was locally observable at either of the stations used for reidentification. While previous loop based reidentification work was limited to dual loop detectors, the present effort also extends the methodology to single loop detectors; thereby making it more widely applicable. Although the research uses loop detector data, the algorithm would be equally applicable to data obtained from many other traffic detectors that provide reproducible vehicle features.
Automobile driving in monotonous situations such as driving for long periods and/or travelling a familiar route may cause the lowering of the driver’s awareness level or what we term here as a Driver’s Activation State (DAS), resulting in an increased risk of an accident. We propose here to develop means with which to create an in-car environment so as to allow active driving, hopefully thus avoiding potentially dangerous situations. In order ultimately to develop a validated activation method, we firstly set out to examine physiological variables, including cardiovascular parameters, during simulated monotonous driving. Subsequently, we investigated the derivation of a suitable DAS index. During the experiment, a momentary electrical test stimulus of 0.5 s duration was applied at a rate of approximately once per 10 min to the subject’s shoulder to evoke a physiological responses. In 11 healthy male volunteers we successfully monitored physiological variables during the experiment and found particular patterns in the beat-by-beat changes of blood pressure in response to the electrical test stimulus. This finding, explained by autonomic activity balance, suggests that the patterns may be used as an appropriate and practicable index relevant to the Driver’s Activation State.
This paper presents a decomposition framework for estimating dynamic origin–destination (O–D) flows on actuation-controlled signalized arterials from link counts, mainly addressing the issues of incomplete information and the large number of O–D pairs. The framework decomposes the original high-dimensional problem into much smaller sub-problems at the intersection and corridor levels. At the intersection level, turning movements are inferred with incomplete information; at the corridor level, the final estimates of O–D flows are constructed as weighted averages of the estimates from the column and row decompositions. Numerical examples are presented to demonstrate the effectiveness and the computational efficiency of the decomposition framework.
In this paper, we present a network level model to describe the information propagation in vehicular ad hoc networks (VANETs). The approach utilizes an existing one-dimensional propagation model to evaluate information travel times on the individual arcs of the network. Traffic flow characteristics are evaluated by a static traffic assignment model. Upper and lower bounds are developed for the time of information propagation between two nodes in a network. We show that the bounds yield good (typically within 5%) estimates of the true time lag for the lower penetration rates (<10%), which makes them particularly useful in the initial deployment stages of vehicle-to-vehicle (V2V) communication. Furthermore, our lower bound reveals that – quite surprisingly – for sufficiently low penetration rates, more equipped vehicles on the road does not necessarily promote the fast propagation of information. As an application of the bounds, we formulate a resource allocation model in which communication devices can be installed along roads to promote wireless propagation. A set of efficient heuristic algorithms is developed to solve the resource allocation problem. Numerical results are given throughout.
Broadcast capacity of the entire network is one of the fundamental properties of vehicular ad hoc networks (VANETs). It measures how efficiently the information can be transmitted in the network and usually it is limited by the interference between the concurrent transmissions in the physical layer of the network. This study defines the broadcast capacity of vehicular ad hoc network as the maximum successful concurrent transmissions. In other words, we measure the maximum number of packets which can be transmitted in a VANET simultaneously, which characterizes how fast a new message such as a traffic incident can be transmitted in a VANET. Integer programming (IP) models are first developed to explore the maximum number of successful receiving nodes as well as the maximum number of transmitting nodes in a VANET. The models embed an traffic flow model in the optimization problem. Since IP model cannot be efficiently solved as the network size increases, this study develops a statistical model to predict the network capacity based on the significant parameters in the transportation and communication networks. MITSIMLab is used to generate the necessary traffic flow data. Response surface method and linear regression technologies are applied to build the statistical models. Thus, this paper brings together an array of tools to solve the broadcast capacity problem in VANETs. The proposed methodology provides an efficient approach to estimate the performance of a VANET in real-time, which will impact the efficacy of travel decision making.
This paper presents a wavelet-based novel freeway automated incident detection algorithm with varying threshold parameters considering the level of traffic flow. In this approach, new test statistics for incident detection are extracted from occupancy and speed data using discrete wavelet transform, which decomposes traffic measurements into different resolution-time components. Unlike conventional incident detection algorithms, which apply fixed threshold values and often result in undesirably high false alarm rates, our proposed algorithm varies its threshold values adaptively based on the level of traffic volume. We have derived the mathematical relationship between the false alarm probability and the threshold value of our proposed decision function. For a given target false alarm rate, the threshold values can be changed adaptively depending on the traffic levels of normal traffic conditions. Also, we propose the new feature selection technique to measure the quality of different features that may be used to discriminate between normal and incident traffic conditions. Using both simulated data set and real-life incident data set, the performance of our proposed algorithm was compared with existing popular approaches such as California algorithm, Minnesota algorithm, conventional neural networks algorithm, and a wavelet-based neural-net algorithm. Experimental results show that the proposed wavelet-based algorithm consistently outperformed others with a higher detection rate, lower false alarm rate, and shorter mean time to detection. It is conclusive that the proposed algorithm is a superior alternative to existing algorithms.
This paper investigates the use of constructive probabilistic neural network (CPNN) in freeway incident detection, including model development and adaptation. The CPNN was structured based on mixture Gaussian model and trained by a dynamic decay adjustment algorithm. The model was first trained and evaluated on a simulated incident database in Singapore. The adaptation of CPNN on the I-880 freeway in California was then investigated in both on-line and off-line environments. This paper also compares the performance of the CPNN model with a basic probabilistic neural network (BPNN) model. The results show that CPNN has three main advantages over BPNN: (1) CPNN has clustering ability and therefore could achieve similarly good incident-detection performance with a much smaller network size; (2) each Gaussian component in CPNN has its own smoothing parameter that can be obtained by the dynamic decay adjustment algorithm with a few epochs of training; and (3) the CPNN adaptation methods have the ability to prune obsolete Gaussian components and therefore the size of the network is always within control. CPNN has shown to have better application potentials than BPNN in this research.
Adaptive cruise control (ACC) provides assistance to the driver in the task of longitudinal control of their vehicle during motorway driving. The system controls the accelerator, engine powertrain and vehicle brakes to maintain a desired time-gap to the vehicle ahead. This research describes the results of a detailed microscopic simulation investigation into the potential impacts of ACC on motorway driving. In addition to simulation, real vehicle driving profiles, obtained from instrumented vehicle experiments in three European countries, have been used to compare real following behaviour with that of a simulated ACC equipped vehicle. This new approach has shown that following with an ACC system can provide considerable reductions in the variation of acceleration compared to manual driving. This indicates a potential comfort gain for the driver and environmental benefits. A number of critical situations in which ACC does not perform well have also been identified. The research also highlights the limitations of microscopic simulation in modelling the impacts of ACC because of the lack of understanding of the interaction between the driver and the ACC system relative to the traffic conditions.
We present an adaptive cruise control (ACC) strategy where the acceleration characteristics, that is, the driving style automatically adapts to different traffic situations. The three components of the concept are the ACC itself, implemented in the form of a car-following model, an algorithm for the automatic real-time detection of the traffic situation based on local information, and a strategy matrix to adapt the driving characteristics (that is, the parameters of the ACC controller) to the traffic conditions. Optionally, inter-vehicle and infrastructure-to-car communication can be used to improve the accuracy of determining the traffic states. Within a microscopic simulation framework, we have simulated the complete concept on a road section with an on-ramp bottleneck, using empirical loop-detector data for an afternoon rush-hour as input for the upstream boundary. We found that the ACC vehicles improve the traffic stability and the dynamic road capacity. While traffic congestion in the reference scenario was completely eliminated when simulating a proportion of 25% ACC vehicles, travel times were already significantly reduced for much lower penetration rates. The efficiency of the proposed driving strategy even for low market penetrations is a promising result for a successful application in future driver assistance systems.
This paper is concerned with the traffic flow stability/instability induced by a particular adaptive cruise control (ACC) policy, known as the “constant time headway (CTH) policy”. The control policy is analyzed for a circular highway using three different traffic models, namely a microscopic model, a spatially discrete model, and a spatially continuous model. It is shown that these three different modeling paradigms can result in different traffic stability properties unless the control policy and traffic dynamics are consistently abstracted for the different paradigms. The traffic dynamics will, however, be qualitatively consistent across the three modeling paradigms if a consistent biasing strategy is used to adapt the CTH policy to the various modeling frameworks. The biasing strategy determines whether the feedback quantity for use in the control, is taken colocatedly, downstream or upstream to the vehicle/section/highway location. For ACC vehicles equipped with forward looking sensors, the downstream biasing strategy should be used. In this case, the CTH policy induces exponentially stable traffic flow on a circular highway in all three modeling frameworks. It is also shown that traffic flow stability will be preserved for an open stretch highway if the entry and exit conditions are made to observe the downstream biasing strategy.
Forward collision warning (FCW) systems can reduce rear-end vehicle collisions. However, if the presentation of warnings is perceived as mistimed, trust in the system is diminished and drivers become less likely to respond appropriately. In this driving simulator investigation, 45 drivers experienced two FCW systems: a non-adaptive and an adaptive FCW that adjusted the timing of its alarms according to each individual driver’s reaction time. Whilst all drivers benefited in terms of improved safety from both FCW systems, non-aggressive drivers (low sensation seeking, long followers) did not display a preference to the adaptive FCW over its non-adaptive equivalent. Furthermore, there was little evidence to suggest that the non-aggressive drivers’ performance differed with either system. Benefits of the adaptive system were demonstrated for aggressive drivers (high sensation seeking, short followers). Even though both systems reduced their likelihood of a crash to a similar extent, the aggressive drivers rated each FCW more poorly than their non-aggressive contemporaries. However, this group, with their greater risk of involvement in rear-end collisions, reported a preference for the adaptive system as they found it less irritating and stress-inducing. Achieving greater acceptance and hence likely use of a real system is fundamental to good quality FCW design.
This paper presents a novel Adaptive Memory Programming (AMP) solution approach for the Fleet Size and Mix Vehicle Routing Problem with Time Windows (FSMVRPTW). The FSMVRPTW seeks to design a set of depot returning vehicle routes to service a set of customers with known demands, for a heterogeneous fleet of vehicles with different capacities and fixed costs. Each customer is serviced only once by exactly one vehicle, within fixed time intervals that represent the earliest and latest times during the day that service can take place. The objective is to minimize the total transportation costs, or similarly to determine the optimal fleet composition and dimension following least cost vehicle routes. The proposed method utilizes the basic concept of an AMP solution framework equipped with a probabilistic semi-parallel construction heuristic, a novel solution re-construction mechanism, an innovative Iterated Tabu Search algorithm tuned for intensification local search and frequency-based long term memory structures. Computational experiments on well-known benchmark data sets illustrate the efficiency and effectiveness of the proposed method. Compared to the current state-of-the-art, the proposed method improves the best reported cumulative and mean results over most problem instances with reasonable computational requirements.
The United States Department of Transportation has recently begun implementation of the national demonstration project for suburban Advanced Traffic Management Systems (ATMS) utilizing the Sydney Coordinated Adaptive Traffic System (SCATS). SCATS is an automated, real time, traffic responsive signal control strategy. The expected benefit from the system comes from its ability to constantly modify signal timing patterns to most effectively accommodate changing traffic conditions. The objectives of this research study were to analyze the differences in certain delay parameters which would occur as a result of implementing SCATS signal control. The study employed a macroscopic simulation procedure to compute intersection delay under both a strategy that changed signal timings once per hour and SCATS signal control. A comparison of delay under both forms of control is presented. The study findings demonstrated mixed results regarding the benefit of SCATS control. A general conclusion of the study was that SCATS distributed the delay across competing approaches more evenly. However, in some cases this resulted in an increase in the total intersection delay. The observed delay change was attributed primarily to the saturation equalization objective of the SCATS control program. SCATS attempts to allocate green time to the intersection approaches based on the degree of saturation. Under this philosophy the system is able to balance the percentage of green time between all approaches, resulting in more uniform delay.
This paper deals with route choice models capturing travelers’ strategic behavior when adapting to revealed traffic conditions en route in a stochastic network. The strategic adaptive behavior is conceptualized as a routing policy, defined as a decision rule that maps from all possible revealed traffic conditions to the choices of next link out of decision nodes, given information access assumptions. In this paper, we use a specialized example where a variable message sign provides information about congestion status on outgoing links. We view the problem as choice under risk and present a routing policy choice model based on the cumulative prospect theory (CPT), where utility functions are nonlinear in probabilities and thus flexible attitudes toward risk can be captured.In order to illustrate the differences between routing policy and non-adaptive path choice models as well as differences between models based on expected utility (EU) theory and CPT, we estimate models based on synthetic data and compare them in terms of prediction results. There are large differences in path share predictions and the results demonstrate the flexibility of the CPT model to represent varying degrees of risk aversion and risk seeking depending on the outcome probabilities.
Frequently implemented at freeway accesses to streamline traffic, ramp-metering control strategy is often implemented during rush hours in heavily congested areas. This paper presents a novel ramp-metering control model capable of optimizing mainline traffic by providing metering rates for accesses within the control segments. Based on Payne's continuum traffic stream model, a linear dynamic model with a quadratic objective function is constructed for integrated-responsive ramp-metering control. Incorporating on-line origin–destination (OD) estimation of co-ordinated interchanges into the proposed model increases efficiency of the control. In addition, an iterative algorithm is proposed to obtain the optimal solution. Simulation results demonstrate the robustness of the proposed model and its ability to streamline freeway traffic while avoiding traffic congestion.
This paper describes a general approach for real time traffic management support using knowledge based models. Recognizing that human intervention is usually required to apply the current automatic traffic control systems, it is argued that there is a need for an additional intelligent layer to help operators to understand traffic problems and to make the best choice of strategic control actions that modify the assumption framework of the existing systems. The need for an open architecture is stated, in order to allow users to modify decision criteria according to their experience, given that no skills are available yet to deal with real time strategy decision making. An architecture of knowledge is described that is oriented towards traffic management strategic advice applied in the TRYS system developed by the authors. This system has been installed for urban motorway control in several Spanish cities. Finally, an example of knowledge-based modeling, using TRYS, is presented in a case study where both the TRYS model and its operation are described. It is concluded that such an approach is feasible, and is compatible with existing state of the art traffic control systems.
An adaptive control model of a network of signalized intersections is proposed based on a discrete-time, stationary, Markov decision process. The model incorporates probabilistic forecasts of individual vehicle actuations at downstream inductance loop detectors that are derived from a macroscopic link transfer function. The model is tested both on a typical isolated traffic intersection and a simple network comprised of five four-legged signalized intersections, and compared to full-actuated control. Analyses of simulation results using this approach show significant improvement over traditional full-actuated control, especially for the case of high volume, but not saturated, traffic demand.
Traffic flow propagation stability is concerned about whether a traffic flow perturbation will propagate and form a traffic shockwave. In this paper, we discuss a general approach to the macroscopic traffic flow propagation stability for adaptive cruise controlled (ACC) vehicles. We present a macroscopic model with velocity saturation for traffic flow in which each individual vehicle is controlled by an adaptive cruise control spacing policy. A nonlinear traffic flow stability criterion is investigated using a wavefront expansion technique. Quantitative relationships between traffic flow stability and model parameters (such as traffic flow and speed, etc.) are derived for a generalized ACC traffic flow model. The newly derived stability results are in agreement with previously derived results that were obtained using both microscopic and macroscopic models with a constant time headway (CTH) policy. Moreover, the stability results derived in this paper provide sufficient and necessary conditions for ACC traffic flow stability and can be used to design other ACC spacing policies.
This paper describes the results of an investigation of the factors that shape the decision by passenger car users of a tolled facility to use an electronic toll collection (ETC) system. The paper is based on revealed data collected in the New York City area, that were analysed with the assistance of discrete choice models. The results provide insight into the nature of the underlying decision making process. A number of results stand out. The first one is that the time saved by using ETC at the toll booths does not play a statistically significant role in the decision to use ETC. This result is conceptually correct because: (1) the time saved at the toll booths when using ETC is a relatively small portion of the total travel time in the hyper-congested network in the New York City area and (2) the actual time saved is curtailed by upstream congestion that makes it difficult for drivers to reach the ETC-only toll booths, and by downstream congestion that frequently prevents driver from crossing the toll booths. The second set of results is related to the perceived financial benefits of using ETC. Two variables were found to play a significant role in increasing ETC use: the toll savings (the difference between cash and ETC tolls), and drivers’ awareness of the toll discounts offered. Other socio-economic attributes have a direct relationship with the likelihood of using ETC: the number of trips made through the facilities, auto ownership, college education, age, and income. The results also indicate significant differences by ethnicity as racial minorities (i.e., African–Americans and Hispanics) are less inclined to use ETC than Caucasians. From the policy standpoint, these results imply that in order to increase ETC use, the difference between cash and ETC tolls should be increased, and accompanied with outreach campaigns to raise awareness of the toll discounts available to ETC users. The underrepresentation of ethnic minorities should also be addressed by implementing specially designed outreach efforts to gain insight into the factors that shape their attitude toward ETC. Taken together, the results in the paper—and in a companion paper that focuses on freight users—provide clear suggestions that could be used to increase ETC use, and enable the transportation system to fully exploit the potential benefits of such an important technology.
This study is a subsequent development of the dynamic evolution model of the market penetration of advanced traveler information systems (ATIS) proposed by Yang and Meng [Transport. Res. A 35 (2001) 895]. In previous study we have shown that a benefit-driven, user-optimal ATIS market does not necessarily lead to a socially optimal growth and optimal stationary equilibrium level of market penetration of ATIS products or services. The current study proposes an optimal time-dependent service pricing strategy so as to minimize total system cost throughout the time horizon of growth or optimally reach a socially desirable target level of ATIS market penetration in a final stationary equilibrium. We formulate the problem of interest as an optimal control problem and propose an efficient solution algorithm together with a numerical demonstration of the characteristics of the study problem.
The objective of this research is to understand the demand for information technology among trucking companies. A multivariate discrete choice model is estimated on data from a large-scale survey of the trucking industry in California. This model is designed to identify the influences of each of twenty operational characteristics on the propensity to adopt each of seven different information technologies, while simultaneously allowing the seven error terms to be freely correlated. Results showed that the distinction between for-hire and private fleets is paramount, as is size of the fleet and the provision of intermodal maritime and air services.
In the 16th International symposium on transportation and traffic theory, we had presented a novel demand management concept known as the Highway Space Inventory Control System (HSICS). The basic idea of HSICS is that all road users have to make reservations in advance to enter the highway. The system allows highway operators to make real-time decisions whether to accept or reject travellers’ requests to use the highway system in order to achieve certain system-wide objectives. The proposed HSICS model consists of two modules – Highway Allocation System (HAS) and the Highway Reservation System (HRS). The HAS is an off-line module that determines the maximum number of trips from each user class (categorized based on time of departure, vehicle type, vehicle occupancy, and trip distance) to be accepted given a pre-defined demand. It develops the optimal highway allocations for different traffic scenarios. The “traffic scenarios-optimal allocations” data obtained in this way enables the development of HRS. The HRS module operates in the on-line mode, where there is uncertainty in the travel demand, to determine whether a request to make a trip between certain origin–destination pair during a certain time interval is accepted or rejected.
Urban freight transportation constitutes both an extremely important and a rather disturbing activity. Increasingly, one observes efforts to measure and control freight movements within city centers. We introduce a possible organizational and technological framework for the integrated management of urban freight transportation and identify important associated planning and operation issues and models. We then describe a formulation for one of these problems, the design of the proposed logistical structure, and discuss algorithmic and implementation issues. Our model city and challenge is Rome.
A number of studies have evaluated the services provided by Advanced Traveler Information Systems [ATIS] under the assumption that information supplied to drivers would be, in some sense, perfect. However, lack of sufficiently useful data and system design constraints can lead to information that is less than useful to the ATIS user. This paper examines the effects of such imperfection through a simulation-based model that was applied over a part of a large metropolitan area. The model has four basic components: 1.(i) an ATIS structure (that specifies the information-gathering, processing and disseminating aspects of the system)2.(ii) traveler behavior3.(iii) network characteristics4.(iv) vehicle movement logic.
Using a ‘yoked driver’ concept, a number of different route guidance strategies are examined. The results indicate that some strategies that would appear to be desirable are not so. Conversely, under high-congestion situations, strategies can be constructed that come close to ‘rectifying’ completely the effects of information imperfection. Overall the paper reiterates the potential of ATIS if information-giving strategies are designed carefully.
Decisions about implementing Advanced Traveler Information Systems (ATIS) should be based on the individual and social benefits expected from such technologies, which will be strongly dependent on the ways travelers respond to these new information sources. This paper explores the behavioral issues important to understanding traveler reactions to ATIS; it discusses evaluation strategies, including stated preference methods and observation of revealed behavior in laboratory simulations and field tests with various degrees of control and complexity. Advantages and disadvantages of different approaches are reviewed, and the experimental design challenges of site selection, recruitment of test subjects, and measurement of behavior are explored.
This paper analyzes the responses from two nationwide surveys designed to obtain user information requirements for the design of advanced traveler information systems (ATIS) and commercial vehicle operations (CVO) with respect to commercial system operators (dispatchers) and commercial vehicle drivers. A total of 673 returned surveys (348 dispatcher surveys and 325 commercial driver surveys), were used in the analysis. Mathematical models were developed, using a binomial logit to predict whether the commercial driver or dispatcher would use an intelligent transportation system, and an ordered probit to estimate the importance of information (i.e. route and navigation, roadside services, personal communication and road and traffic information) to be provided by in-vehicle information systems. The results of this study provide guidelines for the design of information systems and help define informational requirements for users of ATIS/CVO.
Various combinations of advanced public transportation systems (APTS), including advanced transit information, demand responsive transit, and personal rapid transit, are evaluated by applying traditional travel and emissions criteria as well as consumer welfare and equity criteria. A state-of-the-practice regional travel demand model is used to simulate the travel effects of the APTS technologies in the Sacramento region for the year 2015. A method of obtaining consumer welfare is applied to the mode choice models in the travel model. It was found that APTS technologies, which were simulated in this study to act as feeder service for light rail and bus transit, did not significantly reduce congestion and emissions in the region. This was primarily because the Sacramento region lacks extensive penetration by light rail and bus service. The consumer welfare evaluation, however, showed that all the APTS technology scenarios yielded an economic benefit and were generally equitable even when capital, operation, and maintenance costs were included in the analysis. Further, the analysis showed that advanced transit information service alone produced the greatest increase in consumer welfare. The total yearly difference in benefits among the scenarios would be significant. Thus, it is concluded that the method of obtaining consumer welfare used in this study is a useful analytical tool for identifying optimal bundles of APTS technologies.