Using trip diaries to mitigate route risk and risky ...

Accident Analysis and Prevention xxx (2016) xxx–xxx

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Accident Analysis and Prevention journal homepage: www.elsevier.com/locate/aap

Using trip diaries to mitigate route risk and risky driving behavior among older drivers Rashmi P. Payyanadan a,∗ , Adam Maus a , Fabrizzio A. Sanchez b , John D. Lee a , Lillian Miossi a , Amsale Abera a , Jacob Melvin a , Xufan Wang a a b

Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, 53706, USA Department of Statistics, University of Wisconsin-Madison, Madison, WI, 53706, USA

a r t i c l e

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Article history: Received 30 April 2016 Received in revised form 29 August 2016 Accepted 22 September 2016 Available online xxx Keywords: Older drivers Trip Diary Self-regulation Route risk Retrospective feedback

a b s t r a c t To reduce exposure to risky and challenging driving situations and prolong mobility and independence, older drivers self-regulate their driving behavior. But self-regulation can be challenging because it depends on drivers’ ability to assess their limitations. Studies using self-reports, survey data, and hazard and risk perception tests have shown that driving behavior feedback can help older drivers assess their limitations and adjust their driving behavior. But only limited work has been conducted in developing feedback technology interventions tailored to meet the information needs of older drivers, and the impact these interventions have in helping older drivers self-monitor their driving behavior and risk outcomes. The vehicles of 33 drivers 65 years and older were instrumented with OBD2 devices. Older drivers were provided access to customized web-based Trip Diaries that delivered post-trip feedback of the routes driven, low-risk route alternatives, and frequency of their risky driving behaviors. Data were recorded over four months, with baseline driving behavior collected for one month. Generalized linear mixed effects regression models assessed the effects of post-trip feedback on the route risk and driving behaviors of older drivers. Results showed that post-trip feedback reduced the estimated route risk of older drivers by 2.9% per week, and reduced their speeding frequency on average by 0.9% per week. Overall, the Trip Diary feedback reduced the expected crash rate from 1 in 6172 trips to 1 in 7173 trips, and the expected speeding frequency from 46% to 39%. Thus providing older drivers with tailored feedback of their driving behavior and crash risk could help them appropriately self-regulate their driving behavior, and improve their crash risk outcomes. © 2016 Elsevier Ltd. All rights reserved.

1. Introduction Driving a route whether driving straight, negotiating a turn, crossing an intersection, or changing lanes can result in a potential crash. The National Highway Traffic Safety Administration (NHTSA) examines FARS (Fatality Analysis Reporting) and GES (General Estimates System) to determine the types of driving conditions, driving behaviors, and road infrastructure that contribute to crashes (Stutts et al., 2009). In 2012, the NHTSA report showed that for two-vehicle crashes, older drivers were 75% more likely to be involved in a crash between 2 p.m. and 6 p.m., and during daylight – attributed to their increased driving exposure during the day. Older drivers are also involved in greater number of intersection and crossing-related crashes (Hakamies-Blomqvist, 1993) as a result of their increased exposure to intersections due to choice of road type, such as the

∗ Corresponding author. E-mail address: [email protected] (R.P. Payyanadan).

preference to avoid highways (Langford and Koppel, 2006). The 2012 NHTSA report showed that left turn crashes were particularly high for older drivers, with 20% of drivers 70–79 years, and 25% of drivers 80 years and older involved in a left turn crash. Whereas crashes related to driving straight, passing and overtaking were the only driving maneuvers not associated with an increase in crash risk among older drivers. This is because of their self-regulatory behavior and avoidance of lane change maneuvers unless they were confident, and deemed it necessary and safe (Stutts et al., 2009). These results suggest that the risk of crash among older drivers under certain driving situations is partly influenced by their driving exposure patterns and exposure reduction strategies, which are critical but also challenging to measure. While FARS and GES provide adequate population exposure for assessing the crash risk of older drivers, the ability to estimate and account for self-regulation strategies as a measure of crash risk is limited. Driving strategies and compensatory mechanisms to reduce crash risk depends on the driver’s perception of their driving ability. Early studies commonly used self-ratings to assess driver’s

http://dx.doi.org/10.1016/j.aap.2016.09.023 0001-4575/© 2016 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Payyanadan, R.P., et al., Using trip diaries to mitigate route risk and risky driving behavior among older drivers. Accid. Anal. Prev. (2016), http://dx.doi.org/10.1016/j.aap.2016.09.023

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R.P. Payyanadan et al. / Accident Analysis and Prevention xxx (2016) xxx–xxx

self-regulation strategies. These studies showed that older drivers perceived their risk of accident involvement to be significantly lower than other drivers of their same age group when it came to night driving, and driving on wet and snow covered roads (Finn and Bragg, 1986), perceived themselves less likely to be involved in an accident than younger drivers (18–24 years) (Jonah and Dawson, 1982), and less concerned about impaired driving and less likely to think that impaired driving could result in a crash compared to younger drivers (Wilson, 1984). But current studies have shown strong evidence to support that self-enhancement bias is common among drivers of all age groups (Freund et al., 2005; Horswill et al., 2004; White et al., 2011), and has little correlation with actual onroad driving and simulator performance (De Craen et al., 2011; Freund et al., 2005). Thus, while studies using self-ratings have provided important insights into the attitudes and beliefs of driver’s risk perception; using self-ratings to assess self-regulation driving strategies, compensatory mechanisms, and crash risk reduction is often misleading because most drivers, including older drivers overestimate their driving performance (Freund et al., 2005). Recent studies have used drivers’ hazard detection as an indicator of crash risk. A comparison of the driving hazard detection between younger (17–18 years), experienced (22–30 years) and older (65–72 years) drivers showed that experienced and older drivers better anticipated potentially hazardous situations in their driving environment, and continued to scan the environment for potential hazards after a planned event, compared to younger drivers (Borowsky et al., 2010). But hazard perception has been shown to decline with age. Staplin et al. (2012) showed that deficits related to attention, executive function, range of motion, and spatial abilities, increases the likelihood of crashes among older drivers, with reduced contrast sensitivity and Useful Field of View (UFOV) being strong predictors of hazard perception (Horswill et al., 2008). Yet despite age-related declines, older drivers do not have higher crashes than other age-groups (Hakamies-Blomqvist et al., 2002; Langford et al., 2006). This may be due to the fact that when older drivers are aware of their physiological, cognitive, and functional decline, they tend to drive more cautiously, and avoid risky driving situations, restrict their overall driving, and actively regulate their driving behavior (Marottoli and Richardson, 1998; Molnar and Eby, 2008). But evidence to suggest a positive impact of self-regulation on reducing crash risk is not yet clear (Man-Son-Hing et al., 2007). Studies have shown that older drivers who self-regulated their driving behavior reported fewer crashes than those who did not restrict their driving (Holland and Rabbitt, 1992). Whereas others have shown that when at-risk older drivers self-regulated due to poor UFOV scores, they were still twice as likely to incur at-fault crashes over five years compared to low-risk drivers (Ross et al., 2009). These studies have raised three main concerns: a) that older drivers’ do not realize their decline in driving skills to take the necessary self-regulatory action, b) their ability to self-regulate does not match their decline in driving skills, and c) self-regulation is not effective in reducing their crash risk outcomes. The ineffectiveness of self-regulation to reduce crash risk outcomes have been commonly observed in two situations: when cognitive impairments hinder the ability of older drivers to be truly aware of their driving behavior, and when self-regulation strategies do not generate frequent enough behavioral changes to prevent crash involvement (Owsley et al., 2004). But for older drivers who are unaware of their declining ability, or their self-regulation does not compensate for the particular declining ability – providing targeted feedback of their driving behavior may help increase their self-awareness and knowledge of driving safety needs, and improve their driving outcomes. Results from a study by Eby et al. (2003) showed that older drivers who were given the Driving Decisions Workbook to make

them aware of their decline, reported being more aware of their deficits, and began regulating their driving behavior to improve their driving safety. According to the Multifactorial Model for Enabling Driving Safety, matching older driver’s driving ability to their functional capacity can be attained by providing them with the means to accurately assess and evaluate their decline, and accordingly adapt their driving behavior (Anstey et al., 2005). Holland and Rabbitt (1992) showed that two-thirds of the older drivers who were given results of their eyesight and hearing test, made changes to their driving behavior by avoiding driving at night, being cautious when crossing complex or unfamiliar junctions, avoiding rush hour, planning trips in advance, and choosing safer routes. These studies reveal that while older drivers tend to be more cautious and safe, they may be unaware of the risk-reducing alternatives due to a lack of knowledge of the typical crash profile of older drivers, along with the general tendency to stick to familiar routes. With the emergence of in-vehicle data recorders (IVDR), such technologies are making it possible to collect continuous data on true driving behavior, such as risky driving behavior, engagement in secondary tasks, and driver responses (Dingus et al., 2006); to provide more objective feedback to drivers. The goal of this study is to determine whether using personal Trip Diaries to provide older drivers with feedback of their route choice, driving behavior, and low-risk route alternatives (Payyanadan et al., 2016), can result in a reduction of route risk and risky driving behavior. Understanding the influence of the Trip Diary as a feedback tool on driving safety outcomes can aid in the development of more customizable feedback interventions, and targeted driving safety programs to improve the safety and mobility outcomes of older drivers.

1.1. Trip Diary – a web-based feedback for older drivers A web-based Trip Diary was developed for older drivers (Fig. 1). The Trip Diary provided feedback of the driver’s routes, alternate route options with fewer left turns, U-turns, lane closures, and traffic incidents, and a map with directions of the low-risk route alternatives. The Trip Diary also reported the number of left turns, U-turns, speeding, hard braking, harsh cornering, and hard accelerating events by a driver along a driven route. The events were annotated on the map to provide visual feedback of the areas that the driver had a risky driving behavior. To provide the low-risk route alternative, a route risk measure developed by Payyanadan et al. (2016) was implemented in the Trip Diary. The route risk measure compares the crash risk of the routes driven to those suggested by Google and MapQuest. The route with the lowest crash risk was provided as the low-risk route alternative (Fig. 2). The route risk measure considers the number of left turns, U-turns, traffic incidents, and lane closures along a route with minimal cost to distance relative to the route driven. Left turns, Uturns, travel distance, and alternate route options information were retrieved from the recorded driver data, Google and MapQuest APIs, and the traffic incidents and lane closures for a route were retrieved from the 511 Real Time Traffic Map of the Wisconsin Department of Transportation (with permission and licensing agreement from the Wisconsin Department of Transportation).

2. Methods Older drivers 65 years and above were recruited for the study. OBD2 (on-board diagnostic) devices were installed in each of their cars to collect baseline and treatment data. Baseline period was determined based on the power analyses conducted on data collected from a previous study by Payyanadan et al. (2016).



Fig. 1. A web-based Trip Diary developed for older drivers to provide low-risk route alternatives (route with fewer driving challenges), and driving behavior feedback.

Fig. 2. Low-risk route alternative (black route on map) along with the driving directions.


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2.1. Participants A total of 33 drivers 65 and older were recruited from rural (28%), urban (42%), and suburban (30%) settings from a Midwestern state. Participant’s age ranged from 65 to 82 years, and consisted of 15 males and 18 females. County coordinators working at the Aging and Disability Resource Center (ADRC) in each of these regions helped raise awareness about the study. To participate in the study, older adults were required to hold a valid driver’s license, have internet access, and drive at least twice a week. Older adults who provided their contact information to the ADRC county coordinators with interest to participate in the study were contacted by the research team, and participated in the study if they met the inclusion criteria. The vehicles of the 33 participants were instrumented with OBD2 devices for a period of four months, with baseline data collected for a period of one month. During the baseline period, participants were not given access to their driving information. This was to ensure that their baseline route risk and driving behavior was accurately captured. After a month, participants were given access to a customized web-based Trip Diary for a period of three months – where they could log on and access their trip details. Demographic data along with the trip details for baseline and treatment periods are shown in Table 1. 2.2. Device, installation and sensitivity metrics For the study, Geotab GO6 OBD2 devices were purchased from Sprint. The device involved a simple plug-and-play installation – allowing instant trip data access and notification updates to the Trip Diary page. Once the device was installed, participants were asked to go about their normal driving routine. Data collected by the device for analysis and feedback are shown in Table 2, along with the corresponding sensitivity metrics. The sensitivity metrics of the Geotab GO6 OBD2 device were adjusted for passenger vehicle settings recommended by Geotab. 2.3. Trip Diary use and feedback Once baseline period was completed, participants were contacted for a second visit. During the second visit, participants were given access to their Trip Diary page, along with instructions on how to use and understand their trip feedback information on the Trip Diary page. A Trip Diary instructions booklet was provided with steps and explanation on how to use and interpret their trip feedback. Participants were asked to log in to the Trip Diary page daily or at least 2–3 times a week to access feedback about their trips, driving behavior, and alternate route options. During the second visit, 21% of the participants reported limited experience using the internet. To ensure that all participants were able to receive their trip feedback, reports of their Trip Diary feedback information were developed using R (R Development Core Team, 2013) Sweave package and Latex (Leisch, 2002), and mailed to all the participants every month (Fig. 3). Mailed reports consisted of their monthly trip summary report (Fig. 3A), their driving behavior for the current month and past month, driving behavior relative to other participants in the study for the same month (Fig. 3B), map and directions of low-risk route alternatives for trips driven that month (Fig. 3C), and three feedback questions on familiarity with the route driven and low-risk route alternative, near misses along the route driven, and usefulness of the Trip Diary feedback. In this paper, the feedback responses were not analyzed. Participants averaged 6.3 visits to the online Trip Diary during the treatment portion of the study. Combined with the monthly mailed reports, participants were presented with feedback an average of 9.3 times. For each online visit, participants averaged 19 page

views and 40.6 min of activity on the Trip Diary. For the monthly mailed reports, participants were asked to mail back the responses to the three feedback questions (that were also asked on the webbased Trip Diary page). For each monthly report, all participants mailed back their responses. In this paper, the feedback responses were not analyzed. 2.4. Data recording The Geotab SDK (software development kit) link was used to collect data recorded by the OBD2 device (Table 2) and read into the Trip Diary page. For each trip, a CSV file was generated containing information displayed on the Trip Diary page (Table 3). The Trip Diary page displayed information pertaining to seatbelt use, driving with the ‘Check Engine’ light on, map of route driven, left turn and U-turn count, speeding, hard braking, hard cornering, and hard acceleration events along the route driven, and map and directions of the low-risk alternative route. 2.5. Variables, model assumptions and hypotheses Due to a low occurrence rate of crashes, directly measuring significant changes in crash risk requires tens of thousands of drives observed over a very long time frame. In many cases this type of study is impractical to pursue and implement. To address this problem a route crash risk measure was developed (see Payyanadan et al., 2016) to estimate changes in crash risk. This measure uses route characteristics such as left turns, distance, and road closures to determine crash risk. To understand the level of risk associated with route choice, this measure is also applied to relevant route alternatives that could have been driven in lieu of the chosen route, and the ratio of the risk of the chosen route to the risk of the alternatives is determined. Thus if feedback results in changes in route choice, the change in crash risk can be estimated using the new ratio of chosen routes to their alternatives. In this study, to measure the risk associated with a participant’s route choice, for each trip, crash risk of the route driven was determined using the route risk measure developed by Payyanadan et al. (2016) and compared to the crash risk of the low-risk alternative route as in (1). Route Risk Ratio =

Crash Riskroute driven Crash Risklow−risk alternative route

(1)

For simplicity route risk ratio will simply be referred to as route risk except when explicit reference to the ratio is needed for clarity. Participants’ baseline route risk and time under treatment are considered to be independent variables. Baseline route risk is estimated using the median route risk among trips taken during the baseline period of the study. Time under treatment is defined as the number of days since providing access to the Trip Diary. The dependent variables are the driving behaviors (Table 2) and route risk for trips taken while under treatment. To determine the effects of the Trip Diary feedback on route risk and driving behavior, the following assumptions were made, a) Older drivers’ route risk is a function of their inherent route choice and the amount of time under treatment. b) Older drivers’ driving behavior are a function of their inherent driving behavior and the amount of time under treatment. c) The older driver population has a mean route risk and response to treatment, but individuals within the population may differ from either. d) Older drivers’ response to treatment depends on their inherent route choice or driving behavior.



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Fig. 3. Dynamic report of participant’s Trip Diary mailed every month. (A) Monthly trip summary report. (B) Comparison of their driving behaviors between the present and past months, comparison of present month to other drivers in the same period, and whether their current driving behavior is better or needed improvement. (C) Trip summaries of alternate routes that were of lower risk along with driving directions.


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Table 1 Mean participant ages and within period driving data from the OBD2 device, grouped by gender. Gender

Total

Age

Males Females

15 18

74 71

Baseline period (1 month)

Treatment period (3 months)

Distance (miles)

Time (minutes)

Distance (miles)

Time (minutes)

7.0 7.3

12.8 13.6

7.9 7.2

13.6 13.4

Table 2 OBD2 device settings and sensitivity metrics. Geotab GO6 OBD2 data

Definition

GPS coordinates Trip start/stop

Latitude and longitude data for location retrieval. Event-based A trip starts when the vehicle starts moving. A stop is recorded when the vehicle ignition is turned off, or when the vehicle has a speed of less than 1 km/h for more than 200 s. Distance travelled for each trip from origin to destination. Miles Time taken to travel for each trip from origin to Seconds destination. Records changes in speed during a trip. m/s2 , Event-based 3-axis accelerometer recordings to determine vehicle Threshold change of 300 milli-G in any acceleration. direction Speed is monitored against the posted road speed. If there 5 mph over the posted speed limit was no data on the posted speed limit for a section of a trip, no speed violation was recorded. G-force exertion set at −0.58 A hard braking incident is recorded when it caused a force of 1/2 G to be exerted on the vehicle. A hard cornering incident is recorded when a hard or G-force exertion set at >0.47 and aggressive turn causes a force greater than 2/5 G to be