Effects of crash warning systems on rear-end crash avoidance behavior under fog conditions

Reduced visibility conditions increase both the probability of rear-end crash occurrences and their severity. Crash warning systems that employ data from connected vehicles have potential to improve vehicle safety by assisting drivers to be aware of the imminent situations ahead in advance and then taking timely crash avoidance action(s). This study provides a driving simulator study to evaluate the effectiveness of the Head-up Display warning system and the audio warning system on drivers’ crash avoidance performance when the leading vehicle makes an emergency stop under fog conditions. Drivers’ throttle release time, brake transition time, perception response time, brake reaction time, minimum modified time-to-collision, and maximum brake pedal pressure are assessed for the analysis. According to the results, the crash warning system can help decrease drivers’ reaction time and reduce the probability of rear-end crashes. In addition, the effects of fog level and drivers’ characteristics including gender and age are also investigated in this study. The findings of this study are helpful to car manufacturers in designing rear-end crash warning systems that enhance the effectiveness of the system’s application under fog conditions.     

Behavior-consistent real-time traffic routing under information provision

The problem addressed here involves a controller seeking to enhance traffic network performance via real-time routing information provision to drivers while explicitly accounting for drivers’ likely reactions towards the information. A fuzzy control modeling approach is used to determine the associated behavior-consistent information-based network control strategies. Experiments are performed to compare the effectiveness of the behavior-consistent approach with traditional dynamic traffic assignment based approaches for deployment. The results show the importance of incorporating driver behavior realistically in the determination of the information strategies. Significant differences in terms of system travel time savings and compliance to the information strategies can be obtained when the behavior-consistent approach is compared to the traditional approaches. The behavior-consistent approach can provide more robust performance compared to the standard user or system optimal information strategies. Subject to a meaningful estimation of driver behavior, it can ensure system performance improvement. By contrast, approaches that do not seek to simultaneously achieve the objectives of the drivers and the controller can potentially deteriorate system performance because the controller may over-recommend or under-recommend some routes, or recommend routes that are not considered by the drivers.     

A lane-change trajectory model from drivers’ vision view

Car-following and Lane-changing are two fundamental tasks during driving. While many car-following models can be applied, relatively, only a few lane-changing models have been developed. Classical lane-changing models mainly focus on drivers’ lane selection and gap acceptance behaviors, but very limited research has paid attention to formulating detailed lane-changing trajectories. This research aims to fill the gap by proposing a lane-changing trajectory model, which is built directly from drivers’ vision view, to model detailed lane-changing trajectories. A large amount of data of reference angles, defined as the angle changes between the drivers’ vision angle and left or right lane line, were first extracted from the videos recorded by the vehicle traveling data recorders (VTDRs) installed in 11 taxies. A comprehensive data analysis indicates that same drivers show similarity of their daily lane-changing habit but with variety, and different drivers’ lane-change trajectory data show different lane-change “personality” including aggressive or non-aggressive behaviors. Based on these findings, this paper then proposed a hyperbolic tangent lane-change trajectory model to describe drivers’ detailed lane-change trajectories. The model is verified using both real data and simulation. The results show the proposed lane-change trajectory model can successfully describe drivers’ lane-changing trajectories. More importantly, some parameters in the model are directly associated to drivers’ driving characteristics during lane-change. With this unique feature, the proposed model can generate driver-specific lane-change trajectories. Such improvement could contribute to the future development of Advanced Driver Assistance Systems (ADAS).     

国内外高级驾驶辅助系统标准测试场景研究

文章围绕前向碰撞预警系统FCW、车道偏离预警系统LDW、主动紧急制动系统AEB、车道保持系统LKA、自适应巡航系统ACC等5种辅助功能,研究各种功能的场景布置,结合国内外现状及实际测试经验,提出场景案例设计建议。     

Manual vs. adaptive cruise control – Can driver’s expectation be matched?

The role of the driver in the longitudinal car following control task will change from operator to supervisor with most of manual control replaced by automation as adaptive cruise control (ACC) technologies become commonplace. The extent to which manual control can be replaced by ACC will be determined by many factors. An important issue is the compatibility between ACC performance and the driver’s expectations.This paper describes the results of a simulation study of the performance of ACC relative to driver expectation. Driver’s expectation is quantitatively defined as the expected deceleration rate for several time-to-collision (TTC) levels, and an absolute minimum TTC that drivers tried to avoid in all cases. A two-level ACC algorithm was used to simulate the performance of an ACC equipped vehicle in various scenarios, and the result was compared to the driver’s expectations. The investigation has focused on scenarios which ACC is able to manage technically, but where driver expectations might be breached.By systematically changing variables such as the parameters of the ACC algorithms, traffic scenarios and time-headway settings, a large number of situations have been tested. The results have revealed that whilst appropriate ACC settings can be found which will meet the driver’s expectations, the ACC settings that are most capable in a range of traffic conditions are not necessarily the most user-friendly. A discussion on the implications of the findings is also presented.     

The effect of travel time variability on route choice decision: a generalized linear mixed model based analysis

The effect of travel time variability (TTV) on route choice behavior is explored in this study. A stated preference survey is conducted to collect behavioral data on Shanghai drivers’ choice between a slow but stable route and a fast but unreliable route. Travel time and TTV are respectively measured by mean and standard deviation of random travel time. The generalized linear mixed model (GLMM) is applied to quantify trade-offs between travel time and TTV. The GLMM based route choice model effectively accounts for correlations among repeated observations from the same respondent, and captures heterogeneity in drivers’ values of TTV. Model estimation results show that, female drivers and drivers with rich driving experience are less likely to choose a route with high TTV; smaller expected travel time of a route increase the probability of its being chosen; all drivers have intrinsic preference for a route with smaller expected travel time, but the degree of preference may vary within the population; TTV on average has negative effects on route choice decision, but a small portion of drivers are risk-prone to choose a fast but unreliable route despite high TTV.     

Using BDI agents to improve driver modelling in a commuter scenario

The use of multi-agent systems to model and to simulate real systems consisting of intelligent entities capable of autonomously co-operating with each other has emerged as an important field of research. This has been applied to a variety of areas, such as social sciences, engineering, and mathematical and physical theories. In this work, we address the complex task of modelling drivers’ behaviour through the use of agent-based techniques. Contemporary traffic systems have experienced considerable changes in the last few years, and the rapid growth of urban areas has challenged scientific and technical communities. Influencing drivers’ behaviour appears as an alternative to traditional approaches to cope with the potential problem of traffic congestion, such as the physical modification of road infrastructures and the improvement of control systems. It arises as one of the underlying ideas of intelligent transportation systems. In order to offer a good means to evaluate the impact that exogenous information may exert on drivers’ decision making, we propose an extension to an existing microscopic simulation model called Dynamic Route Assignment Combining User Learning and microsimulAtion (DRACULA). In this extension, the traffic domain is viewed as a multi-agent world and drivers are endowed with mental attitudes, which allow rational decisions about route choice and departure time. This work is divided into two main parts. The first part describes the original DRACULA framework and the extension proposed to support our agent-based traffic model. The second part is concerned with the reasoning mechanism of drivers modelled by means of a Beliefs, Desires, and Intentions (BDI) architecture. In this part, we use AgentSpeak(L) to specify commuter scenarios and special emphasis is given to departure time and route choices. This paper contributes in that respect by showing a practical way of representing and assessing drivers’ behaviour and the adequacy of using AgentSpeak(L) as a modelling language, as it provides clear and elegant specifications of BDI agents.     

A microscopic simulation model for merging control on a dedicated-lane automated highway system

The automated highway systems (AHS) are not designed as stand-alone transportation facilities. Drivers will by necessity drive from their origins to the AHS entrance, and from the AHS exit to their final destinations. Therefore, the AHS will affect other transportation facilities, and should be integrated with all other facilities in the transportation system. Interfaces create much of the congestion for today’s transportation systems. Likewise, AHS interfaces may cause a similar problem, due to either AHS interactions with conventional systems or internal limitations from AHS merging capabilities. If these problems exist, either the AHS or the conventional road network cannot function properly. Consequently, the system as a whole may break down and the AHS could potentially become a detriment to the overall transportation system.Clearly, not enough is known about the automated merging process to determine what conditions would lead to congestion at interface points. The current macroscopic analysis techniques assume parameters that are not applicable to an AHS, and no detailed AHS merging models have been developed and validated. This paper addresses the interface problem between an AHS, and conventional roadway. Specifically, it presents a microscopic simulation model for one scenario of the automated merging maneuver. The results of the model show that for low flows and conventional highway speeds, an one-lane AHS merging section with a dedicated automated entrance ramp has many similar characteristics as a two-lane conventional freeway with or without fixed-time ramp metering. However, when the conventional freeway starts to “break down” near its capacity, the AHS continues to perform with little delay. The model also validates that the minimum ramp length requirements are a function of the merging vehicle’s speed, the mainline vehicles’ speed, and the acceleration and deceleration rates of the merging vehicle.     

Point-to-point speed enforcement systems: Speed limits design criteria and analysis of drivers’ compliance

Point-to-point (P2P) speed enforcement is a relatively new approach to traffic law enforcement. Its technology allows vehicles whose average speed exceeds the speed limit over the controlled section to be fined. It therefore encourages compliance over distances longer than those where spot enforcement policies have been in place.In this paper, a procedure for consistently setting speed limits with such enforcement systems is proposed. Such a method has been applied to design the speed limits on two motorways in the district of Naples, Italy, where P2P enforcement systems became operational in 2009 and 2010. The speed limits, which were set using the Italian geometric design standard to assess vehicle stability and stopping sight distance, have been compared with those provided by using well-known international standards.The impact of the newly designed speed limits and of the P2P enforcement system on drivers’ speeding behaviour has been quantified for each highway section and vehicle type. In fact, accurate measurements of the average travel speeds of each vehicle crossing the enforced sections, before and after the activation of the system, were available. The migration from the old speed limits with spot speed enforcement to the new approach resulted in a notable increase in drivers’ compliance to the speed limits with a remarkable decrease in both the average of individual speeds and in their standard deviation.In addition, the analysis of 3 years of data shows that a gradual adaptation of drivers’ behaviour to the system took place. In particular, a decreasing compliance to the speed limits points to a non-optimal system management. Finally, the results of a revealed preference survey allowed us to make a behavioural interpretation regarding the significantly different impacts measured on the two motorways.     

Investigating the effect of travel time variability on drivers' route choice decisions in Shanghai,China

Abstract

This paper investigates the effect of travel time variability on drivers' route choice behavior in the context of Shanghai, China. A stated preference survey is conducted to collect drivers' hypothetical choice between two alternative routes with designated unequal travel time and travel time variability. A binary choice model is developed to quantify trade-offs between travel time and travel time variability across various types of drivers. In the model, travel time and travel time variability are, respectively, measured by expectation and standard deviation of random travel time. The model shows that travel time and travel time variability on a route exert similarly negative effects on drivers' route choice behavior. In particular, it is found that middle-age drivers are more sensitive to travel time variability and less likely to choose a route with travel time uncertainty than younger and elder drivers. In addition, it is shown that taxi drivers are more sensitive to travel time and more inclined to choose a route with less travel time. Drivers with rich driving experience are less likely to choose a route with travel time uncertainty.     

Energy and emissions impacts of a freeway-based dynamic eco-driving system

Surface transportation consumes a vast quantity of fuel and accounts for about a third of the US CO₂ emissions. In addition to the use of more fuel-efficient vehicles and carbon-neutral alternative fuels, fuel consumption and CO₂ emissions can be lowered through a variety of strategies that reduce congestion, smooth traffic flow, and reduce excessive vehicle speeds. Eco-driving is one such strategy. It typically consists of changing a person’s driving behavior by providing general static advice to the driver (e.g. do not accelerate too quickly, reduce speeds, etc.). In this study, we investigate the concept of dynamic eco-driving, where advice is given in real-time to drivers changing traffic conditions in the vehicle’s vicinity. This dynamic strategy takes advantage of real-time traffic sensing and telematics, allowing for a traffic management system to monitor traffic speed, density, and flow, and then communicates advice in real-time back to the vehicles. By providing dynamic advice to drivers, approximately 10–20% in fuel savings and lower CO₂ emissions are possible without a significant increase in travel time. Based on simulations, it was found that in general, higher percentage reductions in fuel consumption and CO₂ emission occur during severe compared to less congested scenarios. Real-world experiments have also been carried out, showing similar reductions but to a slightly smaller degree.     

An agent-based approach to modelling driver route choice behaviour under the influence of real-time information

This paper presents an agent-based approach to modelling individual driver behaviour under the influence of real-time traffic information. The driver behaviour models developed in this study are based on a behavioural survey of drivers which was conducted on a congested commuting corridor in Brisbane, Australia. Commuters’ responses to travel information were analysed and a number of discrete choice models were developed to determine the factors influencing drivers’ behaviour and their propensity to change route and adjust travel patterns. Based on the results obtained from the behavioural survey, the agent behaviour parameters which define driver characteristics, knowledge and preferences were identified and their values determined. A case study implementing a simple agent-based route choice decision model within a microscopic traffic simulation tool is also presented. Driver-vehicle units (DVUs) were modelled as autonomous software components that can each be assigned a set of goals to achieve and a database of knowledge comprising certain beliefs, intentions and preferences concerning the driving task. Each DVU provided route choice decision-making capabilities, based on perception of its environment, that were similar to the described intentions of the driver it represented. The case study clearly demonstrated the feasibility of the approach and the potential to develop more complex driver behavioural dynamics based on the belief–desire–intention agent architecture.     

Highway drivers' willingness to pay for speeding violations in Taiwan

This study investigates the effects of various factors on highway drivers' speeding behaviour and estimates the amount of money that a highway driver is willing to pay for speeding violations in Taiwan. The contingent valuation method is applied to measure drivers' preference and to derive the value function of the amount of money. Logit and probit models are estimated to analyse the significant variables influencing speeding behaviour and to calculate the willingness to pay for speeding violations. The research results show that increasing speeding fines is an effective way to reduce illegal driving behaviour on highways. Significant variables affecting highway drivers' speeding behaviour include gender, engine capacity, past offenders in the previous year, and the risk‐seeking characteristics of drivers. Finally, the logit and probit models show that the amounts of money that highway drivers are willing to pay for speeding violations are $US119 and $US116, respectively. Copyright © 2012 John Wiley & Sons, Ltd.     

Effects of intersection field of view on emergent collision avoidance performance at unsignalized intersections: analysis based on driving simulator experiments

Proper intersection sight distance can effectively lower the possibility of intersection accidents. American Association of State Highway and Transportation Officials (2011) provide a series of recommended dimensions of intersection sight triangles for uncontrolled and stop/yield‐controlled intersections. However, in reality, although the actual intersection design for unsignalized intersections satisfies the requirements of sight distance and clear sight triangle in American Association of State Highway and Transportation Officials' guideline, there are still a large number of crashes occurring at unsignalized intersections for drivers running stop/yield signs or failing to slow down. This paper presents a driving simulator study on pre‐crash at intersections under three intersection field of view (IFOV) conditions. The aim was to explore whether better IFOVs at unsignalized intersections improve their emergent collision avoidance performance under an assumption of valid intersection sight distance design. The experimental results show drivers' ability to identify potential hazards to be significantly affected by their IFOVs. As drivers' IFOV improved, drivers were more likely to choose braking actions to avoid collisions. Better IFOVs were also associated with significant increases in brake time to intersection and significant reductions in deceleration rate and crash rate, thus leading to a lower risk of traffic crash involvement. The results indicate that providing a better IFOV for drivers at intersections should be encouraged in practical applications in order to improve drivers' crash avoidance capabilities. Copyright © 2016 John Wiley & Sons, Ltd.     

A field evaluation case study of the environmental and energy impacts of traffic calming

This study quantifies the energy and environmental impact of a selection of traffic calming measures using a combination of second-by-second floating-car global positioning system data and microscopic energy and emission models. It finds that traffic calming may result in negative impacts on vehicle fuel consumption and emission rates if drivers exert aggressive acceleration levels to speed up to their journeys. Consequently by eliminating sharp acceleration maneuvers significant savings in vehicle fuel consumption and emission rates are achievable through driver education. The study also demonstrates that high emitting vehicles produce CO emissions that are up to 25 times higher than normal vehicle emission levels while low emitting vehicles produce emissions that are 15–35% of normal vehicles. The relative increases in vehicle fuel consumption and emission levels associated with the sample traffic calming measures are consistent and similar for normal, low, and high emitting vehicles.     

Validating the results of a route choice simulator

This paper describes the validation of a route choice simulator known as VLADIMIR (Variable Legend Assessment Device for Interactive Measurement of Individual Route choice). VLADIMIR is an interactive computer-based tool designed to study drivers’ route choice behaviour. It has been extensively used to obtain data on route choice in the presence of information sources such as Variable Message Signs or In-Car Navigation devices. The simulator uses a sequence of digitized photographs to portray a real network with junctions, links, landmarks and road signs. Subject drivers are invited to make journeys between specified origins and destinations under a range of travel scenarios, during which the simulator automatically records their route choices. This paper describes validation experiments carried out during the period Summer 1994 to Autumn 1995 and reports on the results obtained. Each experiment involved a comparison of routes selected in real life with those driven under simulated conditions in VLADIMIR. The analysis included investigation of the subjects’ own assessment of the realism of the VLADIMIR routes they had chosen, a comparison of models based on the real life routes with models based on VLADIMIR routes, and a statistical comparison of the two sets of routes. After an extensive series of data collection exercises and analyses, we have concluded that a well designed simulator is able to replicate real life route choices with a very high degree of detail and accuracy. Not only was VLADIMIR able to precisely replicate the route choices of drivers who were familiar with the network but it also appears capable of representing the kind of errors made and route choice strategies adopted by less familiar drivers. Furthermore, evidence is presented to suggest that it can accurately replicate route choice responses to roadside VMS information.     

Drivers’ parking location choice under uncertain parking availability and search times: A stated preference experiment

To assess parking pricing policies and parking information and reservation systems, it is essential to understand how drivers choose their parking location. A key aspect is how drivers’ behave towards uncertainties towards associated search times and finding a vacant parking spot. This study presents the results from a stated preference experiment on the choice behaviour of drivers, in light of these uncertainties. The attribute set was selected based on a literature review, and appended with the probabilities of finding a vacant parking spot upon arrival and after 8 min (and initially also after 4 min, but later dropped to reduce the survey complexity). Efficient Designs were used to create the survey design, where two rounds of pilot studies were conducted to estimate prior coefficients. Data was successfully collected from 397 respondents. Various random utility maximisation (RUM) choice models were estimated, including multinomial logit, nested logit, and mixed logit, as well as models accounting for panel effects. These model analyses show how drivers appear to accept spending time on searching for a vacant parking spot, where parking availability after 8 min ranks second most important factor in determining drivers’ parking decisions, whilst parking availability upon arrival ranks fourth. Furthermore, the inclusion of heterogeneity in preferences and inter-driver differences is found to increase the predictive power of the parking location choice model. The study concludes with an outlook of how these insights into drivers’ parking behaviour can be incorporated into traffic assignment models and used to support parking systems.     

Modeling the effects of traveler information on freeway origin–destination demand prediction

The primary focus of this research is to develop an approach to capture the effect of travel time information on travelers’ route switching behavior in real-time, based on on-line traffic surveillance data. It also presents a freeway Origin–Destination demand prediction algorithm using an adaptive Kalman Filtering technique, where the effect of travel time information on users’ route diversion behavior has been explicitly modeled using a dynamic, aggregate, route diversion model. The inherent dynamic nature of the traffic flow characteristics is captured using a Kalman Filter modeling framework. Changes in drivers’ perceptions, as well as other randomness in the route diversion behavior, have been modeled using an adaptive, aggregate, dynamic linear model where the model parameters are updated on-line using a Bayesian updating approach. The impact of route diversion on freeway Origin–Destination demands has been integrated in the estimation framework. The proposed methodology is evaluated using data obtained from a microscopic traffic simulator, INTEGRATION. Experimental results on a freeway corridor in northwest Indiana establish that significant improvement in Origin–Destination demand prediction can be achieved by explicitly accounting for route diversion behavior.     

Feebates, footprints and highway safety

This paper presents an analysis of a market-based policy aimed at encouraging manufacturers to develop more fuel efficient vehicles without affecting the car buyer’s choice of vehicle size. A vehicle’s size is measured by its “footprint”, the product of track width and wheelbase. Traditional market-based policies to promote higher fuel economy, such as higher gasoline taxes or gas guzzler taxes, also induce motorists to purchase smaller vehicles. Whether or not such policies affect overall road safety remains controversial, however. Feebates, a continuous schedule of new vehicle taxes and rebates as a function of vehicle fuel consumption, can also be made a function of vehicle size, thus removing the incentive to buy a smaller vehicle. A feebate system based on a vehicle’s footprint creates the same incentive to adopt technology to improve fuel economy as simple feebate systems while removing any incentive for manufacturers or consumers to downsize vehicles.     

Sustainability and automobility among the elderly: An international assessment

In the next three decades there will a huge increase in both the absolute number of older people and in their percentage of the populationin almost all Western European countries, North America, and Australia. Most older people will have active lifestyles in which mobility and access play a major role and almost all older men and a majority of older women will be car drivers, used to the convenience and flexibility which the car provides.Using data from the US, Australia, Germany, New Zealand, Norway, and the United Kingdom, the paper shows that, in spite of cultural and policy differences, older people around the world are more likely to have a license, to take more trips, and to do so more often as the driver of a car than older people just a decade ago; they are also less likely to use public transit. These trends have a number of sustainability implications – the most obvious one is increased environmental pollution. For example, even though older people may travel less than younger drivers they may be polluting proportionately more because a) they are less likely to make as great a proportion of trips in public transit as younger people and b) the trips they do make may create more pollutants. In addition, older drivers may incur more wasted miles due to wayfinding errors and trip-scouting behavior. And when older people curtail their driving, younger family members may have to increase (or lengthen) their trip-making to provide needed services or additional transportation.While this paper stresses the environmental problems posed by an aging population, effective strategies arise from a focus on a broader definition of sustainability. The most important approach is to accept the inevitable and work to make the private car "greener" and safer. New transit service concepts and strategic community and neighborhood design and service elements can complement the development of cleaner cars.Although many of the potential strategies are not new, or can be expensive to implement, the convergence of environmental concerns with other problems arising from the automobility of the elderly – including increasing crash rates and serious loss of mobility among those unable to drive – may make these policies more politically viable than in the past.