老年人对交通安全的影响外文文献

Contents lists available at ScienceDirect Safety Science journal homepage Analysis of impact of elderly drivers on traffi c safety using ANN based car following model Meiying Jiana b Jing Shic aInstitute of Transportation Inner Mongolia University Hohhot 010070 China bInner Mongolia Engineering Research Center for Urban Transportation Data Science and Applications Hohhot 010070 China cDepartment of Civil Engineering Tsinghua University Beijing 100084 China A R T I C L E I N F O Keywords Elderly driver Traffi c safety Artifi cial neural network ANN Car following model A B S T R A C T The purpose of this study is to quantitatively investigate the impact of elderly drivers on traffi c safety Based on the data collected from a driving simulator this study utilizes artifi cial neural network ANN to establish a car following model with consideration of elderly drivers The model validation demonstrates that the ANN based car following model has a high fi tting degree and strong predictive capacity Furthermore two surrogate safety indicators number of potential collisions NPC and Time Integrated Time to collision TTT which characterize the number of potential collisions and the degree of potential crash risk respectively are used to quantify the road traffi c safety with elderly drivers The simulation results show that car following behavior of elderly drivers made a strong impact on the stability of the following platoon The increase of elderly drivers could make the variability in the speed of all the followers greater and lead to drastic increases in both NPC and TTT per vehicle per second Besides the two indicators increase fi rstly and then decrease as the stationary time of the fi rst vehicle increases And they decrease as the absolute value of deceleration for the fi rst vehicle increases when the proportion of elderly drivers is higher We hope the results in this study may be helpful to the development of road safety interventions under the aging population background 1 Introduction The number of elderly people aged 65 years or above has been growing gradually over the years In China the percentage of elderly people is projected to increase from 10 5 in 2015 to 24 in 2050 Lahariya 2008 Along with this trend the number of elderly drivers seems to increase signifi cantly as expected Fofanova and Vollrath 2012 Driving is a complex task that always makes a great demand on drivers perception cognition and motor coordination abilities Dukic and Broberg 2012 However all these abilities decrease gradually with increasing age Hence the driving performance of elderly drivers could be aff ected Delhomme et al 2013 and they are always at risk for crashes Along with the higher rate of severe injury and death in road crashes among elderly people Skyving et al 2009 traffi c safety issues of elderly drivers have drawn increasing attention The classical approach used to assess road safety level is the sta tistical analysis based on crashes data Young et al 2014 such as the study to explore safety issues of elderly truck drivers based on two crash databases of NHTSA Newnam et al 2018 However because crashes are small probability events Kim et al 2016 a more eff ective method microscopic traffi c simulation is getting more and more applications in the investigation of traffi c safety Nevertheless there is little informa tion on traffi c simulation models with elderly drivers in existing studies And evaluation of road safety interventions under the aging population background is also diffi cult to carry out To fi ll this gap this study tries to develop the simulation model with consideration of elderly drivers driving performance Since 29 6 of all police reported crashes are rear end collisions Dastrup et al 2009 this study focuses on estab lishing a car following model to assess road safety level under the aging population background With the rapid developments of traffi c data collection and artifi cial intelligence technology the data driven modeling approaches are fa vored by more and more researchers In addition this type of models has advantages of avoiding the manual errors in traditional calibrated models He et al 2015 and incorporating the additional information into models easily Papathanasopoulou and Antoniou 2015 The data driven approach that is often used to model driving behavior is the car following model based on artifi cial neural network ANN However most of the ANN based car following models are limited to leader fol lower pairs Panwai and Dia 2007 Khodayari et al 2012 Zheng et al https doi org 10 1016 j ssci 2019 104536 Received 7 April 2019 Received in revised form 22 October 2019 Accepted 29 October 2019 Corresponding author E mail address jingshi J Shi Safety Science 122 2020 104536 Available online 14 November 2019 0925 7535 2019 Elsevier Ltd All rights reserved T 2013 proposed a neural network based car following model by con sidering reaction delay and extended to simulate the movement of nine vehicles following each other That study greatly promotes the appli cation of the ANN based car following model Recently some related studies also focused on improving the convergence and performance of the car following model based on ANN Colombaroni and Fusco 2014 Wang et al 2017 And model validation showed that the ANN model could yield a good approximation of driving behavior As a result this study will use the ANN to capture the driving behavior of elderly dri vers In conclusion the purpose of this study is to quantitatively in vestigate the impact of elderly drivers on traffi c safety Due to the limitation of historical crashes data and the diffi culty in the evaluation of road safety interventions under the aging population background this study focuses on traffi c safety issues of elderly drivers by using the microscopic traffi c simulation Because of the advantages of avoiding the manual errors and incorporating the additional information an ANN based car following model with consideration of elderly drivers driving performance ANNCF E model is developed Based on the ANNCF E model the impact of elderly drivers on traffi c safety is quantifi ed by introducing the surrogate safety indicators 2 Elderly drivers traffi c safety With the progress of population aging numerous studies have in vestigated the characteristics of elderly drivers to explain their in volvement in road crashes The declines in elderly drivers cognitive abilitiesmay aff ecttheirdrivingperformance Andrewsand Westerman 2012 Shanmugaratnam et al 2010 Some studies found the age related declines in executive functioning Salthouse et al 2003 as well as processing speed Salthouse 1996 such as informa tion processing that become the primary concern in elderly drivers With regards to visual performance the signifi cant age diff erence in the ability to accommodate visually the size of the visual fi eld and the area of interest what the drivers were looking at Dukic and Broberg 2012 were observed which may result in unsafe driving behavior among elderly drivers Furthermore the other concern in elderly drivers is the declines in reaction time to unexpected conditions Makishita and Matsunaga 2008 Hwakyung and Hocheol 2012 and these declines become more obvious with increased workload Sun et al 2016 When it comes to driving behaviors of elderly drivers a variety of age diff erence has been found in previous studies A study by Andrews and Westerman 2012 found the larger fl uctuation in elderly drivers speed based on a driving simulator And this result is in line with the study of Strayer et al 2004 The similar fl uctuation can be observed in the following distance Dastrup et al 2009 The fl uctuation in speed and following distance may increase the risk of road crashes in elderly drivers Moreover these studies also found the signifi cant longer fol lowing distance of elderly drivers Andrews and Westerman 2012 Dastrup et al 2009 In addition a study done in Australia found the relationship be tween road features and elderly drivers crashes Oxley et al 2006 In generally crashes are a result of drivers characteristics road features driving environment and other factors Especially the changes in the movement of the leading vehicles may be an important contributor to rear end collisions Exiting studies whether they are based on the ex perimental data or the fi eld observation data have focused on one or several factors to explain the involvement in road crashes among el derly drivers Obviously the evaluation of traffi c safety with elderly drivers has not been elaborated Then traffi c simulation which provides a way to take into account diff erent traffi c scenarios becomes a good choice in this study Therefore this study makes a preliminary attempt to establish the car following simulation model to investigate elderly drivers traffi c safety quantitatively Especially based on this simulation model the impact of elderly drivers on traffi c safety is quantifi ed and predicted by introducing the surrogate safety indicators Meanwhile the impacts of some traffi c environment factors on road safety level under the aging population background are also analyzed in this study 3 Methodology This study uses the ANN to capture the car following behaviors of elderly and other drivers In order to train the ANN model car fol lowing behavioral data are collected by using a driving simulator Then the surrogate safety indicators are introduced to investigate the impact of elderly drivers on traffi c safety 3 1 ANN The ANN mimics the structure and thinking processes of the human brain and learns the relationship of the input and output variables based on the data collected Due to numerous advantages such as the ability to handle complex and ill defi ned problems the ability to learn from examples and high fault tolerance the ANN has become a rela tively simple and eff ective approach to deal with the large and complex system and is always used in the engineering analysis and predictions Then this study attempts to use the ANN to capture the car following behaviors of elderly drivers and others The structure of classic ANN always consists of an input layer one or more hidden layers and an output layer Each layer is made of several neurons Fig 1 illustrates the structure of a three layer neural network InFig 1 xxx N12 denotetheinputvariables wiNkp 1 2 1 2 ik and bkp 1 2 hk arethe weight value and bias between the input layer and the hidden layer Similarly wkpjq 1 2 1 2 kj and bjq 1 2 oj correspond to the weight value and bias between the hidden layer and the output layer yyy q12 denote output variables In the car fol lowing model the input variables always correspond to dynamic traffi c information received by each driver and output variables often refer to the decision behavior of each driver The translation function of each neuron is to receive inputs and to generate the output Moreover in order to train the ANN eff ectively these functions are required to have derivative everywhere and always use the sigmoid function such as the tangent sigmoid function and logarithmic sigmoid function During the training process all the weight values and biases need to be calibrated and translation functions need to be determined 3 2 Data collection In order to train and test the ANNCF E model a driving simulation Fig 1 Three layer neural network structure with N input variables p neurons in the hidden layer and q neurons in the output layer M Jian and J ShiSafety Science 122 2020 104536 2 experiment is conducted to collect car following data for elderly drivers and other drivers Due to the lower proportion of elderly drivers a total of 32 drivers completed this driving experiment For the sake of comparison the participants were recruited from two diff erent aged samples The group of elderly drivers n 16 aged 60 years or above M 62 SD 2 11 was comprised of the retired teachers of Inner Mongolia University Their mean driving experience was 25years SD 12 21 with more than 60 000 km driven In addition the group of other drivers n 16 aged from 26 to 42 years M 33 SD 5 19 were teachers recruited from Inner Mongolia University Their mean driving experience was 5years SD 3 71 with more than 20 000 km driven This driving task was carried out in the QJ 4B1 type driving simu lator with six degree motion developed by Beijing Sunheart Simulation Technology Ltd see Fig 2 The driving simulator consisted of a BYD F3 full vehicle cab projectors for generating imagers projection screen and computer programs used to design the driving scenario In order to record driver performance and to collect driving parameters electronic sensors as well as cameras were embedded in this full vehicle The vi sual scene was displayed on the arc shaped screen by means of three Epson projectors giving a forward fi eld of view of 180 degree All participants conducted this experiment individually Before the experiment each participant was given 10min training session to make them familiarize with the vehicle controls as well as to achieve a level of handling the driving experiment profi ciently Once the training session was over participants performed four simulated driving sce narios in turn as follows a scenario 1 designed to follow a leading vehicle with the mean speed of 60km h b scenario 2 designed to follow a multi vehicle platoon with the mean speed of 35km h c scenario 3 designed to follow a leading vehicle with the mean speed of 45km h d scenario 4 designed to follow a multi vehicle platoon with the mean speed of 20km h Lots of studies have shown that speed following distance long itudinal acceleration are important indicators to characterize the driving performance Shinar et al 2005 Consequently during each driving task all these data are recorded and calculated at every 1 10 of a second 3 3 Surrogate safety indicators The number of crashes or fatalities is always used to measure elderly drivers traffi c safety Li et al 2003 But this indicator does not work in traffi c simulation A variety of surrogate safety indicators have been used in previous studies Wang and Stamatiadis 2016 Because of the simplicity of computation and the better capacity to refl ect potential crash risk Shahdah et al 2015 Time to Collision TTC which is defi ned as the time interval between two consecutive vehicles before a rear end collision occurs if the two vehicles move forward with their present speeds is used to measure elderly drivers traffi c safety in this study TTC is calculated as follows t xtxtl vtvt vtvtTTC 0 n nn nn nn 1 1 1 1 Here tTTC n denotes the TTC value of vehiclenat timet xt n1 and vt n1 correspond to the location and speed of vehicle n1re spectively Here vehicle n1denotes the leading vehicle of vehiclen l corresponds to the length of a vehicle and l5m in this study In general it is considered that when a TTC is equal or less than the critical value a potential collision would occur And the lower a TTC falls below the critical value the higher probability with which a po tential collision would occur Obviously TTC indicator does not refl ect the degree of potential crash risk Therefore another safety indictor Time Integrated Time to collision TTT Minderhoud and Bovy 2001 is also used in this study to assess road safety level under the aging population background And the calculated expression of TTT is written as follows tttTTT TTC TTC 0 TTC TTC n t T nn 0 2 Here TTC is the critical value During the simulation the number of potential collisions with TTC value equal or less than TTC which is labeled as NPC as well as the total TTT is recorded Based on these two indicators the other two indicators NPC and TTT per vehicle per second can be calculated and further used to investigate the impact of elderly drivers on road traffi c safety 4 Model formulation The ANNCF E model is established and validated here There are two approaches for modeling the car following behavior by using ANN under the aging population background One is establishing a hybrid ANN model based on the comprehensive car following data of elderly drivers and others This approach works well for the small diff erence in car following performance between the two age groups The other is training an ANN model for elderly drivers and the model for other drivers respectively When the car following performance for elderly drivers is signifi cantly diff erent from that for other drivers this ap proach works best And then before modeling the diff erence in car following performance between elderly drivers and others needs to be analyzed Based on this the ANNCF E model is trained and validated 4 1 Age diff erences in car following performance All the car following indicators that include speed following Fig 2 Driving simulator and scenarios M Jian and J ShiSafety Science 122 2020 104536 3 distance and longitudinal acceleration are calculated for each partici pant based on the raw data Then these indicators are compared be tween elderly drivers and other drivers by using a one way analysis of variance ANOVA Table 1 presents the indicators for each group and results of ANOVA The analysis of speed revealed that the mean speed for each scenario was not signifi cantly diff erent However a signifi cant age diff erence in the standard deviation of speed for all scenarios was found see Table 1 Specifi cally elderly drivers showed larger variability in speed than other drivers In addition the diff erence in mean distance headway was signifi cant for all scenarios Elderly drivers maintained the signifi cant longer distance headway than other drivers in all sce narios above Furthermore the age diff erence in the standard deviation of distance headway was also signifi cant Elderly drivers demonstrated larger variability in distance headway compared to other drivers for scenario 1 3 With regards to acceleration only the standard deviation of acceleration was compared here When following a leading vehicle drivers had to constantly a