An analysis of household vehicle holding durations considering intended holding durations

Household vehicle holding durations are examined in this study using panel data. Panel data enable the observation of changes in household vehicle holdings in discrete time periods. If the data set contains retrospective recall data which offer information on the types and the occurrence time points of the transactions since the last survey, direct observation of the transaction process along a continuous time axis is possible. Furthermore, if the data contain information on intentions for future transactions and actual outcomes of the intentions in later waves, the relationship between intended transactions and actual transactions can be observed. In this study, we develop models of actual vehicle holding durations and models of intended vehicle holding durations for the same set of vehicles. Comparing these two sets of models, the effects on household vehicle holding durations of changes in the household’s plans for vehicle holding and unexpected events can be inferred. To represent unaccounted associations between intended vehicle holding durations and actual vehicle holding durations, vehicle specific error components are introduced into the duration models. A non-parametric approach is adopted in model estimation using mass points, which requires no assumption on the distribution of the error components.     

The use of Bayesian statistics to predict patterns of spatial repeatability

Statistical spatial repeatability (SSR) is an extension to the well known concept of spatial repeatability. SSR states that the mean of many patterns of dynamic tyre force applied to a pavement surface is similar for a fleet of trucks of a given type. A model which can accurately predict patterns of SSR could subsequently be used in whole-life pavement deterioration models as a means of describing pavement loading due to a fleet of vehicles. This paper presents a method for predicting patterns of SSR, through the use of a truck fleet model inferred from measurements of dynamic tyre forces. A Bayesian statistical inference algorithm is used to determine the distributions of multiple parameters of a fleet of quarter-car heavy vehicle ride models, based on prior assumed distributions and the set of observed dynamic tyre force from a ‘true’ fleet of one hundred simulated models. Simulated forces are noted at 16 equidistant pavement locations, similar to data from a multiple sensor weigh-in-motion site. It is shown that the fitted model provides excellent agreement in the mean pattern of dynamic force with the originally generated truck fleet. It is shown that good predictions are possible for patterns of SSR on a given section of road for a fleet of similar vehicles. The sensitivity of the model to errors in parameter estimation is discussed, as is the potential for implementation of the method.     

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.     

A structural model of the use of automobiles by households: a case study of urban Australia

Central to the development of transport energy plans are predictions of automobile use. Together with a knowledge of the fuel efficiency of the vehicle fleet, usage acts interdependently to determine the amount of fuel consumed. In this paper we develop an econometric model system at the household level which treats vehicle use, fuel cost, and vehicle fuel efficiency as functionally interdependent. The data is drawn from Wave 1 of a4‐wave panel of Sydney households. The empirical evidence provides new insights into the influences on vehicle use and sets the context for continuing research efforts.     

A double standard model for allocating limited emergency medical service vehicle resources ensuring service reliability

This paper introduces a new double standard model (DSM), along with a genetic algorithm (GA), for solving the emergency medical service (EMS) vehicle allocation problem that ensures acceptable service reliability with limited vehicle resources. Without loss of generality, the model is formulated to address emergency services to human injuries caused by vehicle crashes at intersections within an urban street network. The EMS fleet consists of basic life support (BLS) and advanced life support (ALS) vehicles suited for treating crashes with different severity levels within primary and secondary service coverage standards corresponding to extended response times. The model ensures that all demand sites are covered by at least one EMS vehicle within the secondary standard and a portion of which also meets the service reliability requirement. In addition, a portion of demand sites can be covered by at least one of each type of EMS vehicles within the primary standard. Meanwhile, it aims to achieve maximized coverage of demand sites within the primary standard that complies with the required service reliability. A computational experiment is conducted using 2004–2010 data on top two hundred high crash intersections in the city of Chicago as demand sites for model application. With an EMS fleet size of 15 BLS and 60 ALS ambulances maintained by the Chicago Fire Department, at best 92.4–95.5% of demand could be covered within the secondary standard at 90% of service reliability; and 65.5–68.4% of high severity demand and 50.2–54.5 low severity demand could be covered within the primary standard at 90% of service reliability. The model can help optimize EMS vehicle allocation in urban areas.     

Green vehicle technology to enhance the performance of a European port: A simulation model with a cost-benefit approach

In this paper, we study the impact of using a new intelligent vehicle technology on the performance and total cost of a European port, in comparison with existing vehicle systems like trucks. Intelligent autonomous vehicles (IAVs) are a new type of automated guided vehicles (AGVs) with better maneuverability and a special ability to pick up/drop off containers by themselves. To identify the most economical fleet size for each type of vehicle to satisfy the port’s performance target, and also to compare their impact on the performance/cost of container terminals, we developed a discrete-event simulation model to simulate all port activities in micro-level (low-level) details. We also developed a cost model to investigate the present values of using two types of vehicle, given the identified fleet size. Results of using the different types of vehicles are then compared based on the given performance measures such as the quay crane net moves per hour and average total discharging/loading time at berth. Besides successfully identifying the optimal fleet size for each type of vehicle, simulation results reveal two findings: first, even when not utilising their ability to pick up/drop off containers, the IAVs still have similar efficacy to regular trucks thanks to their better maneuverability. Second, enabling IAVs’ ability to pick up/drop off containers significantly improves the port performance. Given the best configuration and fleet size as identified by the simulation, we use the developed cost model to estimate the total cost needed for each type of vehicle to meet the performance target. Finally, we study the performance of the case study port with advanced real-time vehicle dispatching/scheduling and container placement strategies. This study reveals that the case study port can greatly benefit from upgrading its current vehicle dispatching/scheduling strategy to a more advanced one.     

A modeling approach for matching ridesharing trips within macroscopic travel demand models

State of the art travel demand models for urban areas typically distinguish four or five main modes: walking, cycling, public transport and car. The mode car can be further split into car-driver and car-passenger. As the importance of ridesharing may increase in the coming years, ridesharing should be addressed as an additional sub or main mode in travel demand modeling. This requires an algorithm for matching the trips of suppliers (typically car drivers) and demanders (travelers of non-car modes). The paper presents a matching algorithm, which can be integrated in existing travel demand models. The algorithm works likewise with integer demand, which is typical for agent-based microscopic models, and with non-integer demand occurring in travel demand matrices of a macroscopic model. The algorithm compares two path sets of suppliers and demanders. The representation of a path in the road network is reduced from a sequence of links to a sequence of zones. The zones act as a buffer along the path, where demanders can be picked up. The travel demand model of the Stuttgart Region serves as an application example. The study estimates that the entire travel demand of all motorized modes in the Stuttgart Region could be transported by 7% of the current car fleet with 65% of the current vehicle distance traveled, if all travelers were willing to either use ridesharing vehicles with 6 seats or traditional rail.     

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.     

Modeling interdependencies between vehicle transaction,residential relocation and job change

This paper introduces a vehicle transaction timing model which is conditional on household residential and job relocation timings. Further, the household residential location and members’ job relocation timing decisions are jointly estimated. Some researchers have modeled the household vehicle ownership decision jointly with other household decisions like vehicle type choice or VMT; however, these models were basically static and changes in household taste over time has been ignored in nearly all of these models. The proposed model is a dynamic joint model in which the effects of land-use, economy and disaggregate travel activity attributes on the major household decisions; residential location and members’ job relocation timing decisions for wife and husband of the household, are estimated. Each of these models is estimated using both the Weibull and log-logistic baseline hazard functions to assess the usefulness of a non-monotonic rather than monotonic baseline hazard function. The last three waves of the Puget Sound Panel Survey data and land-use, transportation, and built environment variables from the Seattle Metropolitan Area are used in this study as these waves include useful explanatory variables like household tenure that were not included in the previous waves.     

Impacts of built environment and emerging green technologies on daily transportation greenhouse gas emissions in Quebec cities: a disaggregate modeling approach

This paper aims to investigate the impact of the built environment (BE) and emerging transit and car technologies on household transport-related greenhouse gas emissions (GHGs) across three urban regions. Trip-level GHG emissions are first estimated by combining different data sources such as origin–destination (OD) surveys, vehicle fleet fuel consumption rates, and transit ridership data. BE indicators for the different urban regions are generated for each household and the impact of neighborhood typologies is derived based on these indicators. A traditional ordinary least square (OLS) regression approach is then used to investigate the direct association between the BE indicators, socio-demographics, and household GHGs. The effect of neighborhood typologies on GHGs is explored using both OLS and a simultaneous equation modeling approach. Once the best models are determined for each urban region, the potential impact of BE is determined through elasticities and compared with the impact of technological improvements. For this, various fuel efficiency scenarios are formulated and the reductions on household GHGs are determined. Once the potential impact of green transit and car technologies is determined, the results are compared to those related to BE initiatives. Among other results, it is found that BE attributes have a statistically significant effect on GHGs. However, the elasticities are very small, as reported in several previous studies. For instance, a 10 % increase in population density will result in 3.5, 1.5 and 1.4 % reduction in Montreal, Quebec and Sherbrooke, respectively. It is also important to highlight the significant variation of household GHGs among neighborhoods in the same city, variation which is much greater than among cities. In the short term, improvements on the private passenger vehicle fleet are expected to be much more significant than BE and green transit technologies. However, the combined effect of BE strategies and private-motor vehicle technological improvement would result in more significant GHGs reductions in the long term.     

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.     

Improving the behavioral realism of global integrated assessment models: An application to consumers’ vehicle choices

A large body of transport sector-focused research recognizes the complexity of human behavior in relation to mobility. Yet, global integrated assessment models (IAMs), which are widely used to evaluate the costs, potentials, and consequences of different greenhouse gas emission trajectories over the medium-to-long term, typically represent behavior and the end use of energy as a simple rational choice between available alternatives, even though abundant empirical evidence shows that real-world decision making is more complex and less routinely rational. This paper demonstrates the value of incorporating certain features of consumer behavior in IAMs, focusing on light-duty vehicle (LDV) purchase decisions. An innovative model formulation is developed to represent heterogeneous consumer groups with varying preferences for vehicle novelty, range, refueling/recharging availability, and variety. The formulation is then implemented in the transport module of MESSAGE-Transport, a global IAM, although it also has the generic flexibility to be applied in energy-economy models with varying set-ups. Comparison of conventional and ‘behaviorally-realistic’ model runs with respect to vehicle purchase decisions shows that consumer preferences may slow down the transition to alternative fuel (low-carbon) vehicles. Consequently, stronger price-based incentives and/or non-price based measures may be needed to transform the global fleet of passenger vehicles, at least in the initial market phases of novel alternatives. Otherwise, the mitigation burden borne by other transport sub-sectors and other energy sectors could be higher than previously estimated. More generally, capturing behavioral features of energy consumers in global IAMs increases their usefulness to policy makers by allowing a more realistic assessment of a more diverse suite of policies.     

Social and spatial effects of transforming the private vehicle fleet in Brisbane,Australia

Transformation of the motor vehicle fleet has been an important feature of the world’s peak car phenomenon. Very few urban transport studies have explored such important changes in large urban cities. Using an innovative green vehicle datasets constructed for 2009 and 2014, this paper investigates the ongoing change in urban private vehicle fleet efficiency (VFE) in Brisbane. The spatial patterns of VFE change were examined with social-spatial characteristics of the urban area. The results showed that the social and spatial effect of VFE changes remain uneven over urban space. The inner urban areas have experienced higher level of VFE change, whilst people in the outer and oil vulnerable areas showed a low tendency in shifting to more efficient vehicles. The implication of VFE change for future household vehicle adoption was also evaluated based on a cost-benefit analysis of new vehicle technology costs and expected fuel savings for households that choose a fuel efficient vehicle. The results show that imposing a stronger national fuel economy target in the long term would accelerate evolution of vehicle fleets and oil vulnerability reduction in Brisbane.     

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.

Linking land use with household vehicle emissions in the central puget sound: methodological framework and findings

A leading cause of air pollution in many urban regions is mobile source emissions that are largely attributable to household vehicle travel. While household travel patterns have been previously related with land use in the literature (Crane, R., 1996. Journal of the American Planning Association 62 (1, Winter); Cervero, R. and Kockelman, C., 1997. Transportation Research Part D 2 (3), 199–219), little work has been conducted that effectively extends this relationship to vehicle emissions. This paper describes a methodology for quantifying relationships between land use, travel choices, and vehicle emissions within the Seattle, Washington region. Our analysis incorporates land use measures of density and mix which affect the proximity of trip origins to destinations; a measure of connectivity which impacts the directness and completeness of pedestrian and motorized linkages; vehicle trip generation by operating mode; vehicle miles/h of travel and speed; and estimated household vehicle emissions of nitrogen oxides, volatile organic compounds, and carbon monoxide. The data used for this project consists of the Puget Sound Transportation Panel Travel Survey, the 1990 US Census, employment density data from the Washington State Employment Security Office, and information on Seattle’s vehicle fleet mix and climatological attributes provided by the Washington State Department of Ecology. Analyses are based on a cross-sectional research design in which comparisons are made of variations in household travel demand and emissions across alternative urban form typologies. Base emission rates from MOBILE5a and separate engine start rates are used to calculate total vehicle emissions in grams accounting for fleet characteristics and other inputs reflecting adopted transportation control measures. Emissions per trip are based on the network distance of each trip, average travel speed, and a multi-stage engine operating mode (cold start, hot start, and stabilized) function.     

Spatio-temporal analysis of car distance,greenhouse gases and the effect of built environment: A latent class regression analysis

This work examines the temporal–spatial variations of daily automobile distance traveled and greenhouse gas emissions (GHGs) and their association with built environment attributes and household socio-demographics. A GHGs household inventory is determined using link-level average speeds for a large and representative sample of households in three origin–destination surveys (1998, 2003 and 2008) in Montreal, Canada. For the emission inventories, different sources of data are combined including link-level average speeds in the network, vehicle occupancy levels and fuel consumption characteristics of the vehicle fleet. Urban form indicators over time such as population density, land use mix and transit accessibility are generated for each household in each of the three waves. A latent class (LC) regression modeling framework is then implemented to investigate the association of built environment and socio-demographics with GHGs and automobile distance traveled. Among other results, it is found that population density, transit accessibility and land-use mix have small but statistically significant negative impact on GHGs and car usage. Despite that this is in accordance with past studies, the estimated elasticities are greater than those reported in the literature for North American cities. Moreover, different household subpopulations are identified in which the effect of built environment varies significantly. Also, a reduction of the average GHGs at the household level is observed over time. According to our estimates, households produced 15% and 10% more GHGs in 1998 and 2003 respectively, compared to 2008. This reduction can be associated to the improvement of the fuel economy of vehicle fleet and the decrease of motor-vehicle usage – e.g., a decrease of 4% is observed for fuel efficiency rates and 12% for distance according to the raw average estimates from 1998 with respect to 2008. A strong link is also observed between socio-demographics and the two travel outcomes. While number of workers is positively associated with car distance and GHGs, low and medium income households pollute less than high-income households.     

An econometric model of vehicle use in the household sector

Vehicle-use modelling at the household level has taken on new importance with the pressures on governments to encourage more efficient utilisation of increasingly scarce nonreplenishible liquid fuels. The fundamental energy equation recognizes two direct influences on consumption—the fuel efficiency of the vehicle and the amount of use. Until recently, the interrelationship between vehicle choice and vehicle utilisation at the household level was acknowledged but ignored. The availability of reliable vehicle-use data at the household level now enables a more serious effort at amending the imbalance of research effort where the reliance has been predominantly on vehicle choice modelling and gross (exogenous) assumptions on utilisation as a basis for predicting fuel consumption. This paper proposes an econometric method for identifying the influences on household vehicle use. It differs from previous empirical work in that vehicle kilometers, fuel cost per kilometer and vehicle fuel efficiency are endogenous, with utilisation of each vehicle endogeneously dependent on the utilisation of each and every household vehicle. The data are drawn from wave 1 of a four-wave panel of 1436 households in the Sydney metropolitan area. The empirical findings expose a set of influences on use hitherto not considered. The model specification provides an appropriate module for integration with household-based discrete choice models of vehicle choice.     

Evolution of the household vehicle fleet: Anticipating fleet composition, PHEV adoption and GHG emissions in Austin, Texas

In today’s world of volatile fuel prices and climate concerns, there is little study on the relationship between vehicle ownership patterns and attitudes toward vehicle cost (including fuel prices and feebates) and vehicle technologies. This work provides new data on ownership decisions and owner preferences under various scenarios, coupled with calibrated models to microsimulate Austin’s personal-fleet evolution.Opinion survey results suggest that most Austinites (63%, population-corrected share) support a feebate policy to favor more fuel efficient vehicles. Top purchase criteria are price, type/class, and fuel economy. Most (56%) respondents also indicated that they would consider purchasing a Plug-in Hybrid Electric Vehicle (PHEV) if it were to cost $6000 more than its conventional, gasoline-powered counterpart. And many respond strongly to signals on the external (health and climate) costs of a vehicle’s emissions, more strongly than they respond to information on fuel cost savings.Twenty five-year simulations of Austin’s household vehicle fleet suggest that, under all scenarios modeled, Austin’s vehicle usage levels (measured in total vehicle miles traveled or VMT) are predicted to increase overall, along with average vehicle ownership levels (both per household and per capita). Under a feebate, HEVs, PHEVs and Smart Cars are estimated to represent 25% of the fleet’s VMT by simulation year 25; this scenario is predicted to raise total regional VMT slightly (just 2.32%, by simulation year 25), relative to the trend scenario, while reducing CO₂ emissions only slightly (by 5.62%, relative to trend). Doubling the trend-case gas price to $5/gallon is simulated to reduce the year-25 vehicle use levels by 24% and CO₂ emissions by 30% (relative to trend).Two- and three-vehicle households are simulated to be the highest adopters of HEVs and PHEVs across all scenarios. The combined share of vans, pickup trucks, sport utility vehicles (SUVs), and cross-over utility vehicles (CUVs) is lowest under the feebate scenario, at 35% (versus 47% in Austin’s current household fleet). Feebate-policy receipts are forecasted to exceed rebates in each simulation year.In the longer term, gas price dynamics, tax incentives, feebates and purchase prices along with new technologies, government-industry partnerships, and more accurate information on range and recharging times (which increase customer confidence in EV technologies) should have added effects on energy dependence and greenhouse gas emissions.     

Modeling traffic's flow-density relation: Accommodation of multiple flow regimes and traveler types

This research investigates freeway-flow impacts of different traveler types by specifying and applying a latent-segmentation model of congested and uncongested driving behaviors. Drivers in uncongested conditions are assumed to drive at self-chosen speeds, while drivers in congested conditions are assumed to take speed as given and choose a spacing (between their vehicle and the previous vehicle). Several classes of driver-vehicle combinations are distinguished in a data set based on double-loop-detector pulses and a household travel survey. These classifications are made on the basis of vehicle type and gender, leading to class estimates of speeds and spacings. The segmentation model is specified as a logit function of density, weather, and vehicle type, leading to estimates of congested-condition probabilities. Unobserved heterogeneity is incorporated in all models via common error assumptions.Results indicate that segmentation models are promising tools for traffic data analysis and that information on travelers, their vehicles, and weather conditions explains significant variation in flow data. By clarifying a greater understanding of traffic conditions and traveler behavior explains much scatter in the fundamental relation between flow, speed, and density, can assist regions in their traffic-management efforts and engineers in their design of roadway facilities. Ultimately, such improvements to travel networks should enhance quality of life.