The impact of transportation alternatives on the decision to cease driving by older adults in Japan

This study investigated whether the availability of transportation alternatives for older drivers is a determinant of the decision to cease driving. We recruited participants from a total of 7827 drivers aged 69 years or older living in Ibaraki Prefecture, Japan who were scheduled to renew their driving license between February and April 2011. In November 2010, we distributed questionnaires to collect data on predictors of driving cessation, and again in December 2012 to determine who had actually ceased driving. The relative impacts of factors related to driving cessation were then estimated. Of the 3089 respondents, 157 did not renew their license. The strongest determinants of this decision were having been advised to stop driving and if they had developed less confidence in their ability to drive safely. Even so, respondents were far more likely to have actually stopped driving if they were sure that someone else was available to provide a ride when they needed one. The final decision to stop driving is strongly influenced by personal convenience based on private transport, especially amongst drivers who have been advised to stop. The availability of public transport alternatives is not as important a factor in this decision.     

Investigating the temporal changes in the relationships between time spent on the internet and non-mandatory activity-travel time use

The amount of time we spend online has been increasing dramatically, influencing our daily travel and activity patterns. However, empirical studies on changes in the extent to which the amount of time spent online are related to changes in our activity and travel patterns are scarce, mainly due to a lack of available longitudinal or quasi-longitudinal data. This paper explores how the relationships between the time spent using the Internet, and the time spent on non-mandatory maintenance and leisure activities, have evolved over a decade. Maintenance activities include out-of-home activities such as shopping, banking, and doctor visits, while leisure activities include entertainment activities, visiting friends, sporting activities, and so forth. Our approach uses two datasets from two major cross-sectional surveys in Scotland, i.e. the 2005/06 Scottish Household Survey (SHS) and the 2015 Integrated Multimedia City Data (iMCD) Survey, which were similarly structured and formed. The multiple discrete–continuous extreme value (MDCEV) model and difference-in-differences (DD) estimation are applied and integrated to examine how the relationships between the time spent on the Internet and travel have changed over time and the direction and magnitude of the changes. Our findings suggest that the complementary associations between Internet use and individuals’ non-mandatory activity-travel time use are diminishing over time, whereas their substitutive associations are increasing. We additionally find that such temporal changes are significant in the case of those who spent moderate to high levels of time on the Internet (5 h or more online) per week.

     

Estimating potential increases in travel with autonomous vehicles for the non-driving,elderly and people with travel-restrictive medical conditions

Automated vehicles represent a technology that promises to increase mobility for many groups, including the senior population (those over age 65) but also for non-drivers and people with medical conditions. This paper estimates bounds on the potential increases in travel in a fully automated vehicle environment due to an increase in mobility from the non-driving and senior populations and people with travel-restrictive medical conditions. In addition, these bounding estimates indicate which of these demographics could have the greatest increases in annual vehicle miles traveled (VMT) and highlight those age groups and genders within these populations that could contribute the most to the VMT increases. The data source is the 2009 National Household Transportation Survey (NHTS), which provides information on travel characteristics of the U.S. population. The changes to light-duty VMT are estimated by creating and examining three possible travel demand wedges. In demand wedge one, non-drivers are assumed to travel as much as the drivers within each age group and gender. Demand wedge two assumes that the driving elderly (those over age 65) without medical conditions will travel as much as a younger population within each gender. Demand wedge three makes the assumption that working age adult drivers (19–64) with medical conditions will travel as much as working age adults without medical conditions within each gender, while the driving elderly with medical any travel-restrictive conditions will travel as much as a younger demographic within each gender in a fully automated vehicle environment. The combination of the results from all three demand wedges represents an upper bound of 295 billion miles or a 14% increase in annual light-duty VMT for the US population 19 and older. Since traveling has other costs besides driving effort, these estimates serve to bound the potential increase from these populations to inform the scope of the challenges, rather than forecast specific VMT scenarios.     

What Does Happen to Our Driving When We Get Older?

Abstract

The objective of this paper is to uncover the main antecedents associated with driving errors among older drivers in order to establish a framework for analyzing potential skill improvements using other approaches, for example, games. The article also aims at classifying age-related declines in seniors' abilities associated with their driving performance. The research undertakes an analysis of attempts made through the investigation of the literature. We have systematically searched papers and extracted (1) prevalent driving errors among seniors and (2) underlying age-related declines in seniors' abilities associated with their driving performance. We have also identified some characteristics of the research in this area (e.g. the research methods and geographical distribution of the research). Our expert panel mapped the functional declines in seniors' abilities to the driving errors to identify the relationship between these findings. A reliability test resulted in 81% reliability of findings. We have identified eight groups of driving errors, which could be affected by physical, visual, and cognitive declines among seniors. According to the experts' opinions, the underlying relationship of physical, visual, and cognitive declines among seniors and their driving errors were also presented. Our findings show that there is a potential for the use of innovative interventions such as playing video games to create an inexpensive, motivational, and enjoyable method that may provide a transfer effect to specific driving skills. This could help improve seniors' driving performance by improving specific functional abilities associated with driving.     

A Bayesian Network model for contextual versus non-contextual driving behavior assessment

Driving behavior is generally considered to be one of the most important factors in crash occurrence. This paper aims to evaluate the benefits of utilizing context-relevant information in the driving behavior assessment process (i.e. contextual driving behavior assessment approach). We use a Bayesian Network (BN) model that investigates the relationships between GPS driving observations, individual driving behavior, individual driving risks, and individual crash frequency. In contrast to prior studies without context information (i.e. non-contextual approach), the data used in the BN approach is a combination of contextual features in the surrounding environment that may contribute to crash risk, such as road conditions surrounding the vehicle of interest and dynamic traffic flow information, as well as the non-contextual data such as instantaneous driving speed and the acceleration/deceleration of a vehicle. An information-aggregation mechanism is developed to aggregates massive amounts of vehicle GPS data points, kinematic events and context information into drivel-level data. With the proposed model, driving behavior risks for drivers is assessed and the relationship between contextual driving behavior and crash occurrence is established. The analysis results in the case study section show that the contextual model has significantly better performance than the non-contextual model, and that drivers who drive at a speed faster than others or much slower than the speed limit at the ramp, and with more rapid acceleration or deceleration on freeways are more likely to be involved in crash events. In addition, younger drivers, and female drivers with higher VMT are found to have higher crash risk.     

Are young Americans carless across the United States? A spatial analysis

This study uses the National Household Travel Survey (NHTS) data to investigate the most recent correlates of vehicle ownership among young Americans. This study performs a spatial analysis to examine the potentially non-stationary relationships between sociodemographic factors and vehicle ownership. Consistent with previous studies, modeling results from this study showed that young Americans are more likely to be carless than older adults. The spatial analysis answers the research question – in which regions(s) young Americans are even less likely to have a car. The results highlighted the Northeast states for the young American’s extra-lower vehicle ownership if the influences of all other factors are held constant. The cost of living and availability of transportation alternatives are possible reasons. Further, this study built separate models for young adults (25–34 years old) and three older age groups. The vehicle ownership correlates within the young adults are found to be generally consistent with the correlates among all adults. Among young adults, vehicle ownership is still significantly related to their gender, educational attainment, employment status, household characteristics, and travel demand. However, young adults’ vehicle ownership seems to be less sensitive to household income than mid-age adults’ (35–44 years old), perhaps because young people may not perceive financial stress such as child support and mortgage. This study contributes by using a spatial analysis approach to reveal the non-stationary correlates of vehicle ownership. This approach is useful for future travel behavior research and transportation policy considering the spatial heterogeneity.     

Older People's Mobility: Segments,Factors, Trends

Abstract

The expanding older population is increasingly diverse with regard to, for example, age, income, location, and health. Within transport research, this diversity has recently been addressed in studies that segment the older population into homogeneous groups based on combinations of various demographic, health-related, or transport-related factors. This paper reviews these studies and compares the segments of older people that different studies have identified. First, as a result of a systematic comparison, we identified four generic segments: (1) an active car-oriented segment; (2) a car-dependent segment, restricted in mobility; (3) a mobile multi-modal segment; (4) and a segment depending on public transport and other services. Second, we examined the single factors used in the reviewed segmentation studies, with focus on whether there is evidence in the literature for the factors’ effect on older people's travel behaviour. Based on this, we proposed a theoretical model on how the different determinants work together to form the four mobility patterns related to the identified segments. Finally, based on current trends and expectations, we assessed which segments are likely to increase or decrease in future generations of older people and what should be done to support the multi-optional and independent mobility of older people.     

The 55mph speed limit on US roads: comments on Godwin and Kulash's analysis

There is probably no way to evaluate the impact of the 55 mph limit in 1974 objectively—the necessary data were never collected. Godwin and Kulash assume a current saving of 3000 lives per year. This number is not the objective result of some explicit procedure, but rather the judgement of two conscientious experts; it is not subject to replication by other experts. Furthermore, the quality of their estimate is limited by the quality of the historical studies they are using for input: those studies relied on ‘best guesses’ because they did not have the necessary basic data. No amount of heroic effort by Godwin and Kulash can overcome these inherent deficiencies.

It is almost as difficult to quantify the benefits of the 55 mph limit today. The regression results reported above find no statistically discernible relation between average speed and the fatality rate, though they do show a significant relationship between speed‐variance and the fatality rate. Given this result, we want to know the relationship between the 55 mph limit and speed variance. There is weak evidence that this critical relationship is negative—raising the speed limit a small amount might actually decrease the dispersion of highway speeds.

Whatever the benefits of the 55 limit, its costs are important. On the rural interstate highways it costs about 250 years of extra driving time to save one life. And the policing resources spent on enforcing the 55 mph limit would save more lives if they were allocated to other highway safety measures.     

Utilizing naturalistic driving data for in-depth analysis of driver lane-keeping behavior in rain: Non-parametric MARS and parametric logistic regression modeling approaches

It is known that adverse weather conditions can affect driver performance due to reduction in visibility and slippery surface conditions. Lane keeping is one of the main factors that might be affected by weather conditions. Most of the previous studies on lane keeping have investigated driver lane-keeping performance from driver inattention perspective. In addition, the majority of previous lane-keeping studies have been conducted in controlled environments such as driving simulators. Therefore, there is a lack of studies that investigate driver lane-keeping ability considering adverse weather conditions in naturalistic settings. In this study, the relationship between weather conditions and driver lane-keeping performance was investigated using the SHRP2 naturalistic driving data for 141 drivers between 19 and 89 years of age. Moreover, a threshold was introduced to differentiate lane keeping and lane changing in naturalistic driving data. Two lane-keeping models were developed using the logistic regression and multivariate adaptive regression splines (MARS) to better understand factors affecting driver lane-keeping ability considering adverse weather conditions. The results revealed that heavy rain can significantly increase the standard deviation of lane position (SDLP), which is a very widely used method for analyzing lane-keeping ability. It was also found that traffic conditions, driver age and experience, and posted speed limits have significant effects on driver lane-keeping ability. An interesting finding of this study is that drivers have a better lane-keeping ability in roadways with higher posted speed limits. The results from this study might provide better insights into understanding the complex effect of adverse weather conditions on driver behavior.     

Analysis of three independent real-world driving studies: A data driven and expert analysis approach to determining parameters affecting fuel economy

It is well established that individual variations in driving style have a significant impact on vehicle energy efficiency. The literature shows certain parameters have been linked to good fuel economy, specifically acceleration, throttle use, number of stop/starts and gear change behaviours. The primary aim of this study was to examine what driving parameters are specifically related to good fuel economy using a non-homogeneous extended data set of vehicles and drivers over real-world driving scenarios spanning two countries. The analysis presented in this paper shows how three completely independent studies looking at the same factor (i.e., the influence of driver behaviour on fuel efficiency) can be evaluated, and, despite their notable differences in location, environment, route, vehicle and drivers, can be compared on broadly similar terms. The data from the three studies were analysed in two ways; firstly, using expert analysis and the second a purely data driven approach. The various models and experts concurred that a combination of at least one factor from the each of the categories of vehicle speed, engine speed, acceleration and throttle position were required to accurately predict the impact on fuel economy. The identification of standard deviation of speed as the primary contributing factor to fuel economy, as identified by both the expert and data driven analysis, is also an important finding. Finally, this study has illustrated how various seemingly independent studies can be brought together, analysed as a whole and meaningful conclusions extracted from the combined data set.     

Compliance and data quality in GPS-based studies

Recent years have witnessed a growing volume of papers describing the use of GPS technology and other tracking technologies for obtaining data on time–space activities. These methods have several advantages over traditional methods of time–space data collection in terms of accuracy, resolution and length of the possible data collection period. However, to date, no work has been done on the compliance rates among participants and the resulting validity of the collected data. This paper presents a method that combines the use of a GPS receiver with Radio Frequency Identification (RFID) technology that was implemented in research on time–space activities of elderly persons with cognitive impairment. The method presented in this paper enables monitoring the level of compliance of the participants during their participation in the study and presents a unique opportunity to examine the extent to which participants in a GPS based study are able to comply with study requirements. Healthy older adults and those with cognitive decline were found to be generally compliant with a complex study protocol. These results serve as another step into the acceptance of GPS based studies as a valid methodology for mobility data collection.     

Exploring relationships between driving events identified by in-vehicle data recorders,infrastructure characteristics and road crashes

There is an increasing interest in technology-based solutions that can assist drivers in reducing their risk of involvement in road crashes. Previous studies showed that driving events produced by in-vehicle data recorders (IVDR) are applicable for identification of unsafe driving patterns, while combined examinations of driving events and road infrastructure characteristics are rare. This study explored the relationship between the IVDR-driving events, road characteristics and crashes, to examine a potential of the events for predicting crashes and identification of high-risk locations on the road network. The study database included 3500 segments of the interurban roads in Israel, for which the automatically produced IVDR events were matched with road infrastructure characteristics and crashes. Negative-binomial regression models were adjusted for the relationships between road characteristics and driving events, and subsequently, between events and crashes, given the exposure. Significant impacts were found, yet various event types showed different relations to the infrastructure characteristics and different effects on crashes, on various road types. Better road conditions were associated with a decrease in “braking” events and an increase in the “speed alert” events, where road layout constraints and junction proximity were associated with an opposite effect on events. “Braking” and total events showed better potential for predicting crashes on single-carriageway roads, with a positive link to crashes, where for other road types the “speed alert” events were stronger related to crashes, but with a negative link. The heterogeneity of findings indicates a need in further research of the above relationship, with a particular focus on definitions of driving events produced by the IVDR or other technologies.     

Multi-period Research Designs for Identifying Causal Effects of Built Environment Characteristics on Travel Behaviour

Abstract

To date, most empirical studies have applied cross-sectional designs to investigate the relationship between the built environment (BE) and travel behaviour (TB). Since these studies cannot identify causal influence, the use of designs that provide data on multiple moments in time seems necessary. This article classifies these designs and describes how they can be applied to identify causality in this relationship. We recommend the use of natural experiments to assess the impact of changes in land use/infrastructure and prospective longitudinal designs to assess the impact of residential or job moves. In addition, the role of the BE can be explored by assessing the impact of (1) deliberate TB change experiments and (2) changes in household circumstances across different spatial contexts over time. The use of randomised experimental designs is recommended for the former and prospective longitudinal designs for the latter. The article concludes with an outlook on future research.     

Exploring the impact of connected and autonomous vehicles on freeway capacity using a revised Intelligent Driver Model

ABSTRACT

Connected and autonomous vehicle (CAV) technologies are expected to change driving/vehicle behavior on freeways. This study investigates the impact of CAVs on freeway capacity using a microsimulation tool. A four-lane basic freeway segment is selected as the case study through the Caltrans Performance Measurement System (PeMS). To obtain valid results, various driving behavior parameters are calibrated to the real traffic conditions for human-driven vehicles. In particular, the calibration is conducted using genetic algorithm. A revised Intelligent Driver Model (IDM) is developed and used as the car-following model for CAVs. The simulation is conducted on the basic freeway segment under different penetration rates of CAVs and different freeway speed limits. The results show that with an increase in the market penetration rate, freeway capacity increases, and will increase significantly as the speed limit increases.     

Real world driving cycle for motorcycles in Edinburgh

Knowledge of the driving cycle is an important requirement in the evaluation of exhaust emissions. Data were collected from trips performed on five routes between the home addresses in the surrounding areas and place of work at Napier University in Edinburgh. A real world Edinburgh motorcycle driving cycle (EMDC) is developed for each of the urban and rural roads, using this data. Forty-four trips were made on the routes in both urban and rural areas. We assess motorcycle speed, percentage time spent in cruise, accelerations, decelerations and idling and their statistical validity over trip lengths. The results show that EMDC has a cycle length of 770 and 656 s for urban and rural trips, which are higher than those of the European Commission’s driving cycle for cars used for emission estimations of motorcycles. Time spent in acceleration and deceleration modes of EMDC are found to be significantly higher than in other driving cycle studies, reflecting diverse driving conditions in Edinburgh.     

Active transportation and physical activity: opportunities for collaboration on transportation and public health research

Physically inactive lifestyles are a major public health challenge, and research in the transportation field on influences on the choice to walk and bike may provide guidance toward solutions. In the interests of promoting effective collaboration among the transportation, planning, and health fields, the current paper was written to fulfill three purposes. The first purpose was to summarize the transportation and planning studies on the relation between community design and non-motorized (“active”) transport and to interpret these studies from a health perspective. The second purpose was to summarize studies from the health literature that examine the relation between physical environmental variables and leisure-time physical activity that have relevance for transportation research. The third purpose was to promote more collaboration among transportation, planning, and health investigators by identifying opportunities for transdisciplinary research.     

A Framework for Developing Driving Cycles with On‐Road Driving Data

Abstract

This paper attempts to propose a framework on driving cycle development based on a thorough review of 101 transient driving cycles. A comparison of the driving cycles highlighted that Asian driving is the slowest but most aggressive while European driving is the fastest and smoothest. Further review of the cycle development methodologies identified three major elements for developing a driving cycle; test route selection, data collection and cycle construction methods. A framework was eventually proposed based on these findings and recommendations from this review. First, traffic activity patterns and quantitative statistics should be considered in determining the test routes. Speed data can be collected by using chase car method, on‐board measurement techniques or their hybrid. As for the construction of driving cycle, the matching approach has been more commonly used. It is recommended that the tendency of zero change in acceleration, which has been commonly ignored in the literature, and the application of succession probability at second‐by‐second level should be further explored. A fifth mode, creeping, is also recommended for modal analysis for characterizing urban congested driving conditions.     

Getting around a license-plate ban: Behavioral responses to Mexico City’s driving restriction

License-plate-based driving restrictions are among the highest profile policies for local governments to address congestion and air pollution. Cities as varied as Sao Paulo, Paris, Tianjin, and New Delhi have enacted temporary or permanent restrictions to improve local air quality. Using household travel survey data and a research design based on the abrupt shift in how the policy applies to 10-year-old vs. 9-year-old vehicles, we evaluate the impact of Hoy No Circula, one of the earliest and most studied driving restrictions, in Mexico City. In line with previous studies, we find that Hoy No Circula has done little to reduce overall vehicle travel, but we reject the prevailing theory that its lack of success is due to perverse incentives for households to buy second cars. Instead, we highlight the range of other, less costly ways that people adjust behavior to avoid the restrictions. Although no single behavior dominates, most households — particularly those that own older, higher-polluting vehicles — do not use their car every weekday regardless of the restriction. As a result, it is relatively easy to shuffle travel from restricted days to unrestricted days and thus avoid the ban. Shuffling travel days is less costly, more immediately available, and far simpler for most households than buying a second car.     

Car today,gone tomorrow: The ephemeral car in low-income,immigrant and minority families

Most transportation research in the United States uses cross-sectional, “snapshot” data to understand levels of car access. Might this cross-sectional approach mask considerable variation over time and within households? We use a panel dataset, the Panel Study of Income Dynamics (PSID), for the years 1999–2011 to test this question. We find that for most families, being “carless” is a temporary condition. While 13 % of families in the US are carless in any given year, only 5 % of families are carless for all seven waves of data we examine in the PSID. We also find that poor families, immigrants, and people of color (particularly, blacks) are considerably more likely to transition into and out car ownership frequently and are less likely to have a car in any survey year than are non-poor families, the US-born, and whites.     

Driving cycle prediction model based on bus route features

Bus fuel economy is deeply influenced by the driving cycles, which vary for different route conditions. Buses optimized for a standard driving cycle are not necessarily suitable for actual driving conditions, and, therefore, it is critical to predict the driving cycles based on the route conditions. To conveniently predict representative driving cycles of special bus routes, this paper proposed a prediction model based on bus route features, which supports bus optimization. The relations between 27 inter-station characteristics and bus fuel economy were analyzed. According to the analysis, five inter-station route characteristics were abstracted to represent the bus route features, and four inter-station driving characteristics were abstracted to represent the driving cycle features between bus stations. Inter-station driving characteristic equations were established based on the multiple linear regression, reflecting the linear relationships between the five inter-station route characteristics and the four inter-station driving characteristics. Using kinematic segment classification, a basic driving cycle database was established, including 4704 different transmission matrices. Based on the inter-station driving characteristic equations and the basic driving cycle database, the driving cycle prediction model was developed, generating drive cycles by the iterative Markov chain for the assigned bus lines. The model was finally validated by more than 2 years of acquired data. The experimental results show that the predicted driving cycle is consistent with the historical average velocity profile, and the prediction similarity is 78.69%. The proposed model can be an effective way for the driving cycle prediction of bus routes.