An analysis on long term emission benefits of a government vehicle fleet replacement plan in northern illinois

There have been a number of studies of the effectiveness of vehicle scrappage programs, which offer incentives to accelerated scrappage of older vehicles often thought to be high emitters. These programs are voluntary and aimed at replacement of household vehicles. In contrast, there is a gap in knowledge related to the emissions benefits of government fleet replacement (retirement) programs. In this study, the efficacy of a fleet replacement program for a local government agency in Northern Illinois, the Forest Preserve of DuPage County (FPDC), is examined using a probabilistic vehicle survival model that accounts for time-varying covariates such as vehicle age and gasoline price. The vehicle lifetime operating emissions are calculated based on the estimated vehicle survival probabilities from the survival model and compared with those derived using the EPA default fleet used in MOBILE6 and the fleet represented by the Oak Ridge National Laboratory (ORNL) survival curve. The results suggest that while there may be short term emission benefits of the FPDC fleet replacement plan, the long-term emission benefits are highly sensitive to economic factors (e.g., future gasoline price) and exhibit a decreasing trend. This indicates that an adaptive multi-stage replacement strategy as opposed to a fixed one is preferable to achieve optimal cost effectiveness.

An analysis of the effects of French vehicle inspection program and grant for scrappage on household vehicle transaction

The French government has implemented a periodical vehicle inspection program, which aims at maintaining proper functioning of the vehicle and ensuring the emissions control systems installed on the vehicle work properly. Also, an incentive program for scrapping old vehicles was introduced in 1994 through 1996 to promote the replacement of those vehicles with higher emissions by newer vehicles with lower emissions. A hazard-based duration model of household vehicle transaction behavior has been developed in this study to examine the effects of the inspection program and the grant for scrappage on vehicle transaction timing. The model is developed as a competing risks model assuming the following three types of competing risks: replacing one of the vehicles in the household fleet, disposing of one vehicle in the fleet, and acquiring one vehicle to add to the fleet. The empirical analysis is carried out using the panel data of French households' vehicle ownership from 1984 to 1998, obtained by the panel survey called Parc-Auto, which has been conducted by a French marketing firm, SOFRES, since 1976. The long panel observation period facilitates the introduction of macro-economic indicators into the model, enabling the analysis to distinguish the effects of policy measures from macro-economic factors. The empirical results indicate that the expected vehicle holding duration becomes 1.3 years longer under the inspection program than before the program commenced, given that the vehicle is replaced by another vehicle at the end of the holding duration; and that the conditional probability of replacing a vehicle aged 10 years and over becomes 1.2 times higher, and the average holding duration becomes shorter by 3.3 years, when the grant for scrappage is available.     

Forecasting automobile demand using disaggregate choice models

This paper presents a simulation model of the American automobile market. The simulation model combines a disaggregate model of household automobile number and type choice with an econometric model of used vehicle scrappage and simple models of new car supply. For fixed vehicle designs, consumer and producer interactions determine new car sales, used car scrappage and consumer vehicle holdings. The model allows automobiles to be highly differentiated and consumers to be heterogeneous. Short-run equilibrium is defined as supply equal to demand for every vehicle type during each market period. The automobile stock then evolves slowly as new vehicles are added and old vehicles are removed during each period. An empirical application of the simulation model with 12 consumer groups and 131 vehicle types is used to forecast automobile holdings. A base case scenario is run for 1978–1984 and compared with the observed market behavior during this period. Several other simulations are then run comparing different gasoline price scenarios with the base case for 1984–1990.     

Passing Bays for Slow Moving Vehicles on Rural Two‐lane Roads

Abstract

Slow‐moving vehicles, including agricultural vehicles, on arterial highways can cause serious delays to other traffic as well as posing an extra safety risk. This paper elaborates on a small‐scale solution for these problems: the passing bay. It investigates the impacts of a passing bay on the total delay for other motorized vehicles, the number of passing manoeuvres and hindered vehicles, and the mean delay per hindered vehicle. The latter is also considered to be an indicator for traffic safety. The calculations are performed for two characteristic trips with a slow‐moving vehicle. The passing bay is an effective solution to reducing delays on arterial highways when two‐way hourly volumes exceed 600–1000 vehicles. The effects depend on the trip length and speed of the slow‐moving vehicle, and on the passing sight distance limitations of the road. A distance of 2–4?km between the passing bays seems an acceptable compromise between the reduction of delay for other motorized vehicles and the extra discomfort and delay for drivers of slow‐moving vehicles. This result also shows that passing bays are not effective in regions where slow‐moving vehicles mainly make trips shorter than this distance.     

Analysis of equilibrium automobile holdings in Israel with aggregate discrete choice models

Models of household vehicle ownership decisions do not suffice as a basis for forecasting the size and composition of aggregate vehicle holdings. Forecasting applications require that such models be imbedded in systems describing the operation of the automobile market. This paper presents a new model of short run equilibrium in the automobile market. The short run is a period within which new car designs and prices are fixed but used car prices adjust competitively to market forces. The magnitude and mix of new car sales, the extent of used car scrappage and the composition of used car holdings are determined in equilibrium with used car prices. An econometric version of the market model has been estimated on Israeli data and applied to analyze the impact of vehicle tax policy on automobile holdings in Israel. The paper describes this application.     

Pedestrian gap acceptance for mid-block street crossing

Abstract

This paper investigates pedestrians' traffic gap acceptance for mid-block street crossing in urban areas. A field survey was carried out at an uncontrolled mid-block location in Athens, Greece. Pedestrians' decisions and traffic conditions were videotaped in terms of the size of traffic gaps rejected or accepted, waiting times and crossing attempts and vehicle speeds. A lognormal regression model was developed to examine pedestrian gap acceptance. It was found that gap acceptance was better explained by the distance from the incoming vehicle, rather than its speed. Other significant effects included illegal parking, presence of other pedestrians and incoming vehicles’ size. A binary logistic regression model was developed to examine the effect of traffic gaps and other parameters on pedestrians' decisions to cross the street or not. The results reveal that this decision is affected by the distance from the incoming vehicles and the waiting times of pedestrians.     

Estimating fuel consumption and emissions based on reconstructed vehicle trajectories

Microscopic emission models are widely used in emission estimation and environment evaluation. Traditionally, microscopic traffic simulation models and probe vehicles are two sources of inputs to a microscopic emission model. However, they are not effective in reflecting all vehicles' real‐world operating conditions. Using each vehicle's spot speed data recorded by detectors, this paper provides a new method to estimate all vehicles' real‐world activities data. These data can then be used as inputs to a microscopic emission model to estimate vehicle fuel consumption and emissions. The main task is to reconstruct trajectory of each vehicle and calculate second‐by‐second speed and acceleration from the activities data. The Next Generation Simulation dataset and the Comprehensive Modal Emissions Model are used in this study to calculate and analyze the emission results for both lane‐level and link‐level. The results showed that using the proposed method for estimating vehicle fuel consumption and emissions is promising. Copyright © 2012 John Wiley & Sons, Ltd.     

Differences in single heavy vehicle crashes at intersections and midblocks

The objective of this research is to identify the factors differentiating between single heavy vehicle collisions at intersections and midblocks by using a binary logit model. Our results show that single vehicle crashes involving heavy vehicle at intersections are more likely to occur on main roads and highways, whereas crashes at midblocks are more likely to occur on divided two‐way roads, roads with special facilities or features (e.g. bridge) and roads with a higher percentage of heavy vehicle traffic. Intersection crashes are also more likely to involve vehicles that are turning left or right, resulting in angle crashes and vehicle overturn, whereas midblock crashes are more likely to involve vehicles on higher posted speed roads. Copyright © 2017 John Wiley & Sons, Ltd.     

An integrated model for discrete and continuous decisions with application to vehicle ownership,type and usage choices

This paper proposes an integrated econometric framework for discrete and continuous choice dimensions. The model system is applied to the problem of household vehicle ownership, type and usage. A multinomial probit is used to estimate household vehicle ownership, a multinomial logit is used to estimate the vehicle type (class and vintage) choices, and a regression is used to estimate the vehicle usage decisions. Correlation between the discrete (number of vehicles) and the continuous (total annual miles traveled) parts is captured with a full variance–covariance matrix of the unobserved factors. The model system is estimated using Simulated Log-Likelihood methods on data extracted from the 2009 US National Household Travel Survey and a secondary dataset on vehicle characteristics. Model estimates are applied to evaluate changes in vehicle holding and miles driven, in response to the evolution of social societies, living environment and transportation policies.     

The simple platoon advancement model of its technologies applied to vehicle control at signalised intersections

The Simple Platoon Advancement (SPA) Model describes a conceptual system whose principal objective is to increase the throughput of vehicles at signalised intersections. This is achieved through a novel combination of Intelligent Transport System (ITS) technologies including Automatic Cruise Control, Lane Departure Avoidance, and Collision Avoidance. These are combined in SPA so that vehicles are progressed through signalised intersections under automated control. All of the vehicles in a stationary queue are moved instantly at the start‐of‐green as a closely‐spaced platoon. Dispersion occurs after all vehicles are in motion. Throughput of the SPA model is determined analytically and comparisons are made between the SPA model and a valid representation of current road traffic behaviour. These comparisons show that theoretically a SPA system can progress nearly twice as many vehicles past the stopline as can be seen in today's road network. Other benefits of a conceptual SPA system are improved safety and a reduction in delay per vehicle.     

Rear‐end crash potential estimation in the work zone merging areas

This paper proposed a methodology to estimate rear‐end crash potential of the merging vehicles traveling in the merge lane, on the basis of the traffic data extracting from the available videotapes. First, we developed a binary logit model to identify drivers' merging behavior in the work zone merging area. Subsequently, the occurrence potential of rear‐end crash based on time‐to‐collision was computed between the merging vehicle and its neighboring vehicles. The overall crash potential of the merging vehicle was finally determined. It was found that the crash potential decreases with the remaining distance to work zone. Moreover, there will be a rear‐end crash potential of 4.0% if the merging vehicle fails to complete merging at the end of work zone merging area. If the merging vehicle takes an early merge, there will be a lower rear‐end crash potential (1.2%). These findings suggest that we should encourage merging vehicles to take early merges for improving the traffic safety in the work zone merging areas. Copyright © 2012 John Wiley & Sons, Ltd.     

Optimal scheduling of public transport fleet at network level

This paper deals with the development of a strategic approach for optimizing the operation of public transport system that considers both user's objective and operator's objective. Passengers of public transport are assumed to seek a minimum wait time to conduct the trips, while on the other hand, operators are concerned with the efficient operation such as minimum fleet size. The average minimum wait time is to be achieved by creating an optimal despatching policy for each vehicle from the terminal. As for efficient operation the utilisation of vehicle should be maximised by having a minimum number of vehicles in operation. User's and operator's objectives are optimized within certain operational constraints such as vehicle capacity to maintain acceptable level of service. The i‐model is contructed in a bi‐level programming form in which the user's objective is minimized by dynamic programming and the operator's objective is minimized by various routing strategies. Furthermore, an algorithm and a contrived example are developed to solve and see the performance of the approach.     

Extending Time to Collision for probabilistic reasoning in general traffic scenarios

Vehicle-to-vehicle communication systems allow vehicles to share state information with one another to improve safety and efficiency of transportation networks. One of the key applications of such a system is in the prediction and avoidance of collisions between vehicles. If a method to do this is to succeed it must be robust to measurement uncertainty and to loss of communication links. The method should also be general enough that it does not rely on constraints on vehicle motion for the accuracy of its predictions. It should work for all interactions between vehicles and not just a select subset. This paper presents a method to calculate Time to Collision for unconstrained vehicle motion. This metric is gated using a novel technique based on relative vehicle motion that we call “looming”. Finally, these ideas are integrated into a probabilistic framework that accounts for uncertainty in vehicle state and loss of vehicle-to-vehicle communication. Together this work represents a new way of considering vehicle collision estimation. These algorithms are validated on data collected from real world vehicle trials.     

Developing green fleet management strategies: Repair/retrofit/replacement decisions under environmental regulation

The considerable cost of maintaining large fleets has generated interest in cost minimization strategies. With many related decisions, numerous constraints, and significant sources of uncertainty (e.g. vehicle breakdowns), fleet managers face complex dynamic optimization problems. Existing methodologies frequently make simplifying assumptions or fail to converge quickly for large problems. This paper presents an approximate dynamic programming approach for making vehicle purchase, resale, and retrofit decisions in a fleet setting with stochastic vehicle breakdowns. Value iteration is informed by dual variables from linear programs, as well as other bounds on vehicle shadow prices. Sample problems are based on a government fleet seeking to comply with emissions regulation. The model predicts the expected cost of compliance, the rules the fleet manager will use in deciding how to comply, and the regulation’s impact on the value of vehicles in the fleet. Stricter regulation lowers the value of some vehicle categories while raising the value of others. Such insights can help guide regulators, as well as the fleet managers they oversee. The methodologies developed could be applied more broadly to general multi-asset replacement problems, many of which have similar structures.     

Understanding the Relationship Between Physical and Virtual Representations of Transit Agencies

Abstract

This study examines whether physical attributes of transit agencies, such as agency size, make a difference in how transit websites are designed, and how transit information is distributed. The objective of this study is to see if there is a relationship between physical and virtual representations of transit agencies. A rating instrument is developed for evaluating the quality of transit websites. Our findings suggest that transit agency size plays a key role in determining website quality: When the size of transit system is large, the information about the agency is too complex to be effectively presented on web pages. Thus, the quality of the large agencies’ websites is lower than medium-sized agencies. Instead, we find that large agencies attempt to design more user-friendly sites, and provide advanced information searching tools to compensate for low information quality. Policy implications for transit agencies are discussed.     

Developing freeway lane‐changing support systems using artificial neural networks

Lane‐changing involves many concerns about safety and efficiency which makes it one of the most difficult tasks of driving. It is indeed quite personal since drivers operate vehicles according to their integrated perception of comprehensive circumstances rather than individual rules. A lane‐changing decision support model is developed in this study using artificial neural networks (ANN). The advantages of the ANN approach lie in the learning capability. Due to its nature, an ANN model can consolidate various kinds of information surrounding the vehicle for the drivers and generate reliable results to help control vehicles. It then becomes a useful mechanism to assist drivers in judging current situations and making the right decisions. Several preliminary validations and comparisons are conducted with the field survey data. It is confirmed that the ANN model mimics traffic characteristics more accurately than conventional methods. This product would expedite the implementation of relevant applications in the intelligent transportation systems context. In particular, the ANN model can be adapted to individual driver characteristics. This reveals practical feasibility and significant market potential for customized in‐vehicle equipment.     

User preferences regarding autonomous vehicles

This study gains insight into individual motivations for choosing to own and use autonomous vehicles and develops a model for autonomous vehicle long-term choice decisions. A stated preference questionnaire is distributed to 721 individuals living across Israel and North America. Based on the characteristics of their current commutes, individuals are presented with various scenarios and asked to choose the car they would use for their commute. A vehicle choice model which includes three options is estimated:

• (1)Continue to commute using a regular car that you have in your possession.
• (2)Buy and shift to commuting using a privately-owned autonomous vehicle (PAV).
• (3)Shift to using a shared-autonomous vehicle (SAV), from a fleet of on-demand cars for your commute.

A factor analysis determined five relevant latent variables describing the individuals’ attitudes: technology interest, environmental concern, enjoy driving, public transit attitude, and pro-AV sentiments. The effects that the characteristics of the individual and the autonomous vehicle have on use and acceptance are quantified through random utility models including logit kernel model taking into account panel effects.Currently, large overall hesitations towards autonomous vehicle adoption exist, with 44% of choice decisions remaining regular vehicles. Early AV adopters will likely be young, students, more educated, and spend more time in vehicles. Even if the SAV service were to be completely free, only 75% of individuals would currently be willing to use SAVs. The study also found various differences regarding the preferences of individuals in Israel and North America, namely that Israelis are overall more likely to shift to autonomous vehicles.Methods to encourage SAV use include increasing the costs for regular cars as well as educating the public about the benefits of shared autonomous vehicles.     

Estimating emissions reductions from accelerated vehicle retirement programs

Accelerated vehicle retirement programs offer owners of older vehicles incentives to scrap those vehicles earlier than might otherwise occur. Since older vehicles generally pollute more than newer vehicles, public agencies adopt such programs to reduce air pollutant emissions. Current methods of estimating the emissions reduction benefits of the programs are based on several assumptions and limited empirical evidence. This paper uses data from two large-scale programs in California to demonstrate that changing assumptions can significantly alter the assumed benefits of the program. The results show that vehicle retirement programs are likely to reduce emissions, but probably not as much as expected, particularly for nitrogen oxide and carbon monoxide emissions. The differences in estimates stem from several factors: scrapped vehicles are generally driven fewer miles than other vehicles of the same model year; some of the vehicles would have been scrapped without the program or not have lasted as long as expected; emissions for some pollutants may not be as high as predicted; and replacement vehicles are usually older than the fleet average.     

Vehicle reidentification and travel time measurement across freeway junctions using the existing detector infrastructure

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.     

Including heavy vehicles in a car‐following model: modelling,calibrating and validating

Heavy vehicles influence general traffic in many different ways compared with passenger vehicles, and this may result in different levels of traffic instability. Increases in the number and proportion of heavy vehicles in the traffic stream will therefore result in different traffic flow conditions. This research initially outlines the different car‐following behaviour of drivers in congested heterogeneous traffic conditions indicating the necessity for developing a car‐following model, which includes these differences. A psychophysical car‐following model, similar in form to Weideman's car‐following model, was developed. Due to the complexity of the developed model, the calibration of the model was undertaken using a particle swarm optimisation algorithm with the data recorded under congested traffic conditions. This was then incorporated into a traffic microsimulation model. The results showed that the car‐following perceptual thresholds and thus action points of drivers differ based on their vehicle and the lead vehicle types. The inclusion of the heavy vehicles in the model showed significant impacts on the traffic dynamic and interactions amongst different vehicles. Copyright © 2016 John Wiley & Sons, Ltd.