Delivering improved alerts,warnings, and control assistance using basic safety messages transmitted between connected vehicles

When vehicles share their status information with other vehicles or the infrastructure, driving actions can be planned better, hazards can be identified sooner, and safer responses to hazards are possible. The Safety Pilot Model Deployment (SPMD) is underway in Ann Arbor, Michigan; the purpose is to demonstrate connected technologies in a real-world environment. The core data transmitted through Vehicle-to-Vehicle and Vehicle-to-Infrastructure (or V2V and V2I) applications are called Basic Safety Messages (BSMs), which are transmitted typically at a frequency of 10 Hz. BSMs describe a vehicle’s position (latitude, longitude, and elevation) and motion (heading, speed, and acceleration). This study proposes a data analytic methodology to extract critical information from raw BSM data available from SPMD. A total of 968,522 records of basic safety messages, gathered from 155 trips made by 49 vehicles, was analyzed. The information extracted from BSM data captured extreme driving events such as hard accelerations and braking. This information can be provided to drivers, giving them instantaneous feedback about dangers in surrounding roadway environments; it can also provide control assistance. While extracting critical information from BSMs, this study offers a fundamental understanding of instantaneous driving decisions. Longitudinal and lateral accelerations included in BSMs were specifically investigated. Varying distributions of instantaneous longitudinal and lateral accelerations are quantified. Based on the distributions, the study created a framework for generating alerts/warnings, and control assistance from extreme events, transmittable through V2V and V2I applications. Models were estimated to untangle the correlates of extreme events. The implications of the findings and applications to connected vehicles are discussed in this paper.     

The role of alternative fuel vehicles: Using behavioral and sensor data to model hierarchies in travel

Greater adoption and use of alternative fuel vehicles (AFVs) can be environmentally beneficial and reduce dependence on gasoline. The use of AFVs vis-à-vis conventional gasoline vehicles is not well understood, especially when it comes to travel choices and short-term driving decisions. Using data that contains a sufficiently large number of early AFV adopters (who have overcome obstacles to adoption), this study explores differences in use of AFVs and conventional gasoline vehicles (and hybrid vehicles). The study analyzes large-scale behavioral data integrated with sensor data from global positioning system devices, representing advances in large-scale data analytics. Specifically, it makes sense of data containing 54,043,889 s of speed observations, and 65,652 trips made by 2908 drivers in 5 regions of California. The study answers important research questions about AFV use patterns (e.g., trip frequency and daily vehicle miles traveled) and driving practices. Driving volatility, as one measure of driving practice, is used as a key metric in this study to capture acceleration, and vehicular jerk decisions that exceed certain thresholds during a trip. The results show that AFVs cannot be viewed as monolithic; there are important differences within AFV use, i.e., between plug-in hybrids, battery electric, or compressed natural gas vehicles. Multi-level models are particularly appropriate for analysis, given that the data are nested, i.e., multiple trips are made by different drivers who reside in various regions. Using such models, the study also found that driving volatility varies significantly between trips, driver groups, and regions in California. Some alternative fuel vehicles are associated with calmer driving compared with conventional vehicles. The implications of the results for safety, informed consumer choices and large-scale data analytics are discussed.     

Customizing driving cycles to support vehicle purchase and use decisions: Fuel economy estimation for alternative fuel vehicle users

Wider deployment of alternative fuel vehicles (AFVs) can help with increasing energy security and transitioning to clean vehicles. Ideally, adopters of AFVs are able to maintain the same level of mobility as users of conventional vehicles while reducing energy use and emissions. Greater knowledge of AFV benefits can support consumers’ vehicle purchase and use choices. The Environmental Protection Agency’s fuel economy ratings are a key source of potential benefits of using AFVs. However, the ratings are based on pre-designed and fixed driving cycles applied in laboratory conditions, neglecting the attributes of drivers and vehicle types. While the EPA ratings using pre-designed and fixed driving cycles may be unbiased they are not necessarily precise, owning to large variations in real-life driving. Thus, to better predict fuel economy for individual consumers targeting specific types of vehicles, it is important to find driving cycles that can better represent consumers’ real-world driving practices instead of using pre-designed standard driving cycles. This paper presents a methodology for customizing driving cycles to provide convincing fuel economy predictions that are based on drivers’ characteristics and contemporary real-world driving, along with validation efforts. The methodology takes into account current micro-driving practices in terms of maintaining speed, acceleration, braking, idling, etc., on trips. Specifically, using a large-scale driving data collected by in-vehicle Global Positioning System as part of a travel survey, a micro-trips (building block) library for California drivers is created using 54 million seconds of vehicle trajectories on more than 60,000 trips, made by 3000 drivers. To generate customized driving cycles, a new tool, known as Case Based System for Driving Cycle Design, is developed. These customized cycles can predict fuel economy more precisely for conventional vehicles vis-à-vis AFVs. This is based on a consumer’s similarity in terms of their own and geographical characteristics, with a sample of micro-trips from the case library. The AFV driving cycles, created from real-world driving data, show significant differences from conventional driving cycles currently in use. This further highlights the need to enhance current fuel economy estimations by using customized driving cycles, helping consumers make more informed vehicle purchase and use decisions.     

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.     

Eco-driving key factors that influence fuel consumption in heavy-truck fleets: A Colombian case

This research identifies key variables that influence fuel consumption that might be improved through eco-driving training programs under three circumstances that have been scarcely studied before: (a) heavy- and medium-duty truck fleets, (b) long-distance freight transport, and (c) the Latin American region. Based on statistical analyses that include multivariate regression of operational variables on fuel consumption, the impacts of an eco-driving training campaign were measured by comparing ex ante and ex post data. Operational variables are grouped into driving errors, trip conditions, driver behavior, driver profile, and vehicle attributes.The methodology is applied in a freight fleet with nationwide transport operations located in Colombia, where the steepness of its roads plays an important role in fuel consumption. The fleet, composed of 18 trucks, is equipped with state-of-the-art real-time data logger systems. During four months, 517 trips traveling a total distance of 292,512 km and carrying a total of 10,034 tons were analyzed.The results show a baseline average fuel consumption (FC) of 1.716 liters per ton-100 km. A different logistics performance indicator, which measures FC in liters per ton transported each 100 km, shows an average of 3.115. After the eco-driving campaign, reductions of 6.8% and 5.5% were obtained. Drivers’ experience, driving errors, average speed, and weight-capacity ratio, among others, were found to be highly relevant to FC. In particular, driving errors such as acceleration, braking and speed excesses are the most sensitive to eco-driving training, showing reductions of up to 96% on the average number of events per trip.     

Development of a corrected average speed model for calculating carbon dioxide emissions per link unit on urban roads

The purpose of our study is to develop a “corrected average emission model,” i.e., an improved average speed model that accurately calculates CO₂ emissions on the road. When emissions from the central roads of a city are calculated, the existing average speed model only reflects the driving behavior of a vehicle that accelerates and decelerates due to signals and traffic. Therefore, we verified the accuracy of the average speed model, analyzed the causes of errors based on the instantaneous model utilizing second-by-second data from driving in a city center, and then developed a corrected model that can improve the accuracy. We collected GPS data from probe vehicles, and calculated and analyzed the average emissions and instantaneous emissions per link unit. Our results showed that the average speed model underestimated CO₂ emissions with an increase in acceleration and idle time for a speed range of 20 km/h and below, which is the speed range for traffic congestion. Based on these results, we analyzed the relationship between average emissions and instantaneous emissions according to the average speed per link unit, and we developed a model that performed better with an improved accuracy of calculated CO₂ emissions for 20 km/h and below.     

An integrated traffic-driving simulation framework: Design,implementation, and validation

This paper first describes the process of integrating two distinct transportation simulation platforms, Traffic Simulation models and Driving Simulators, so as to broaden the range of applications for which either type of simulator is applicable. To integrate the two distinct simulation platforms, several technical challenges needed to be overcome including reconciling differences in update frequency, coordinate systems, and the fidelity levels of the vehicle dynamics models and graphical rendering requirements of the two simulators. Following the successful integration, the integrated simulator was validated by having several human subjects drive a 2.5 mile long segment of a signalized arterial in both the virtual environment of the integrated simulator, and in the real-world during the evening “rush hour”. Several aspects of driving behavior were then compared between the human subjects’ driving in the “virtual” and the real world. The comparisons revealed generally similar behavior, in terms of average corridor-level travel time, deceleration/acceleration patterns, lane-changing behavior, as well as energy consumption and emissions production. The paper concludes by suggesting possible extensions of the developed prototype which the researchers are currently pursuing, including integration with a computer networking simulator, to facilitate Connected Vehicle (CV) and Vehicle Ad-hoc Network (VANET) related studies, and a multiple participant component that allows several human drivers to interact simultaneously within the integrated simulator.     

Age related annual crash incidence rate ratios in professional drivers of heavy goods vehicles

ObjectivesEvidence concerning crash risk for older heavy vehicle drivers is sparse, making it difficult to assess if it is prudent to encourage older drivers to remain in the workforce in a climate of labour shortages. The objective of this study was to estimate annual crash rate ratios of older male heavy vehicle drivers relative to their middle aged peers.MethodsData utilized in this study includes all crashes meeting inclusion criteria involving heavy goods vehicles, categorised as rigid trucks and articulated trucks; this data was recorded by the New South Wales Roads and Traffic Authority. The exposure to the risk of a crash was represented by distance travelled for each vehicle type and year, by age of driver, as estimated by the Australian Survey of Motor Vehicle Use. Negative binomial regression modelling was applied to estimate annual crash incidence rate ratios for male drivers in various age groups.ResultsA total of 26,146 crashes occurred in New South Wales during 1999–2006, involving a total of 54,191 vehicles; removing observations that did not meet the inclusion criteria, 19,736 observations remained representing 12,501 crashes. For rigid trucks, the incidence rate ratio for drivers aged 65+ years, compared to 45–54 year olds, was 0.74 (95% CI 0.51, 0.98). For articulated trucks, the annual crash incidence rate ratio for drivers aged 65+ years compared to 45–54 year olds was 1.4 (95% CI 0.96, 1.9), and that for drivers aged 55–64 years compared to 45–54 year olds was 1.1 (95% CI 0.83, 1.3).ConclusionsOlder male professional drivers of heavy goods vehicles have lower risk of crashes in rigid vehicles, possibly due to accrued driving experience and self-selection of healthy individuals remaining in the workforce. Thus, encouraging these drivers to remain in the workforce is appropriate in the climate of labour shortages, as this study provides evidence that to do so would not endanger road safety.     

Effects of driving style on the fuel consumption of city buses under different road conditions and vehicle masses

The variance in fuel consumption caused by driving style (DS) difference exceeds 10% and reaches a maximum of 20% under different road conditions, even for experienced bus drivers. To study the influence of DS on fuel consumption, a method for summarizing DS characteristic parameters on the basis of vehicle-engine combined model is proposed. With this method, the author proposes 26 DS characteristic parameters related to fuel consumption in the accelerating, normal running, and decelerating processes of vehicles. The influence of DS characteristic parameters on fuel consumption under different road conditions and vehicle masses is quantitatively analyzed on the basis of real driving data over 100,000 km. Analysis results show that the influence of DS characteristic parameters on fuel consumption changes with road condition and vehicle mass, with road condition serving a more important function. However, the DS characteristics in the accelerating process of vehicles are decisive for fuel consumption under different conditions. This study also calculates the minimum sample size necessary for analyzing the effect of DS characteristics on fuel consumption. The statistical analysis based on the real driving data over 2500 km can determine the influence of DS on fuel consumption under a given power-train configuration and road condition. The analysis results can be employed to evaluate the fuel consumption of drivers, as well as to guide the design of Driver Advisory System for Eco-driving directly.     

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.     

Controlled mechanical vibration applied to driver’s right heel to sustain alertness: Effects on cardiovascular behavior

Vehicle-related countermeasures to sustain driver’s alertness might improve traffic safety. The purpose of this study was to investigate the effects of somatosensory 20 Hz mechanical vibration, applied to driver’s right heel during prolonged, simulated, monotonous driving, on their cardiovascular hemodynamic behavior. In 12 healthy young male volunteers, during 90-min periods of simulated monotonous driving, we compared cardiovascular variables during application of 20 Hz mechanical vibration with 1.5 Hz as a control and with no vibration. The parameters recorded were indices of key cardiovascular hemodynamic phenomena, i.e., blood pressure as an indicator of stress, cardiac output, and total peripheral-vascular resistance. The principle results were that all conditions increased the mean blood pressure, and elicited a vascular-dominant reaction pattern typically observed in monotonous driving tasks. However, mean blood pressure and total peripheral-vascular resistance during the monotonous task were significantly decreased in those receiving the 20 Hz vibration as compared with 1.5 Hz and with no vibration. The observed differences indicate the cardiovascular system being more relieved from monotonous driving stress with the 20 Hz vibration. The major conclusion is that applying 20 Hz mechanical vibration to the right heel during long-distance driving in non-sleepy drivers could facilitate more physiologically appropriate status for vehicle operation and could be a potential vehicular countermeasure technology.     

Comparative analysis of the energy consumption and CO2 emissions of 40 electric,plug-in hybrid electric,hybrid electric and internal combustion engine vehicles

This paper analyses the results of the Royal Automobile Clubhallo’s 2011 RAC Future Car Challenge, an annual motoring challenge in which participants seek to consume the least energy possible while driving a 92 km route from Brighton to London in the UK. The results reveal that the vehicle’s power train type has the largest impact on energy consumption and emissions. The traction ratio, defined as the fraction of time spent on the accelerator in relation to the driving time, and the amount of regenerative braking have a significant effect on the individual energy consumption of vehicles. In contrast, the average speed does not have a great effect on a vehicles’ energy consumption in the range 25–70 km/h.     

Socially optimal electric driving range of plug-in hybrid electric vehicles

This study determines the optimal electric driving range of plug-in hybrid electric vehicles (PHEVs) that minimizes the daily cost borne by the society when using this technology. An optimization framework is developed and applied to datasets representing the US market. Results indicate that the optimal range is 16 miles with an average social cost of $3.19 per day when exclusively charging at home, compared to $3.27 per day of driving a conventional vehicle. The optimal range is found to be sensitive to the cost of battery packs and the price of gasoline. When workplace charging is available, the optimal electric driving range surprisingly increases from 16 to 22 miles, as larger batteries would allow drivers to better take advantage of the charging opportunities to achieve longer electrified travel distances, yielding social cost savings. If workplace charging is available, the optimal density is to deploy a workplace charger for every 3.66 vehicles. Moreover, the diversification of the battery size, i.e., introducing a pair and triple of electric driving ranges to the market, could further decrease the average societal cost per PHEV by 7.45% and 11.5% respectively.     

Vehicle attribute trade-offs to meet the 2025 CAFE fuel economy target

The literature analyzes changes in vehicle attributes that can improve fuel economy to meet Corporate Average Fuel Economy (CAFE) standards. However, these analyses exclude either vehicle price, size, acceleration or technology advancement. A more comprehensive examination of the trade-offs among these attributes is needed, this case study focuses on technically feasible modifications to a reference 2012 vehicle to meet the 2025 fuel economy target. Scenarios developed to examine uncertainty in technology advancement indicate that expected technology cost reductions over time will be insufficient to offset the costs of additional fuel efficiency technologies that could be used to meet the 2025 fuel economy target while maintaining other vehicle attributes. The mid-price scenario results show the targeted 66% increase in fuel economy from 2012 to 2025 can be achieved with (i) a 10% ($2070) vehicle price increase (lightweight hybrid electric vehicle), (ii) a 31% (2.9 second) increase in the 0–97 km/h (60 mph) acceleration time (smaller engine), or (iii) a 17% (700 L) decrease in interior volume (smaller body) while maintaining other vehicle attributes. These results are consistent with those obtained using methods that generalize the US light-duty vehicle fleet, but are not a forecast of future vehicle attributes because combinations of less perceptible changes to vehicle price, acceleration and size would also be feasible. This study shows there are numerous ways that 2025 fuel economy targets can be met; therefore, the trade-offs quantified provide important insights on the implications of future CAFE standards.     

Implications of driving style and road grade for accurate vehicle activity data and emissions estimates

Real-world vehicle operating mode data (2.5 million 1 Hz records), collected by instrumenting the vehicles of 82 volunteer drivers with OBD datalogger and GPS while they drove their routine travel routes, were analyzed to quantify vehicle emissions estimate errors due to road grade and driving style in rural, hilly Vermont. Data were collected in winter and summer for MY 1996 and newer passenger cars and trucks only. EPA MOVES2010b was used to estimate running exhaust emissions associated with measured vehicle activity. Changes in vehicle specific power (VSP) and MOVES operating mode (OpMode) due to proper accounting for real-world road grade indicated emission rate errors between 10% and 48%, depending on pollutant, chiefly because grade-related changes in VSP could shift activity by as many as six OpModes, depending on road type. The correct MOVES OpMode assignment was made only 33–55% of the time when road grade was not included in the VSP calculation. Driving style of individual drivers was difficult to assess due to unknown traffic operations data, but the largest differences between individual drivers were observed on rural restricted roads, where traffic conditions and control have minimal impact. The results suggest the importance of (1) measuring and incorporating real-world road grade in order to correctly assign MOVES emission rates; and (2) developing a driving style typology to account for differences in the MOVES emissions estimates due to driver variability.     

Fit-for-duty test for estimation of drivers’ sleepiness level: Eye movements improve the sleep/wake predictor

Driver sleepiness contributes to a considerable proportion of road accidents, and a fit-for-duty test able to measure a driver’s sleepiness level might improve traffic safety. The aim of this study was to develop a fit-for-duty test based on eye movement measurements and on the sleep/wake predictor model (SWP, which predicts the sleepiness level) and evaluate the ability to predict severe sleepiness during real road driving. Twenty-four drivers participated in an experimental study which took place partly in the laboratory, where the fit-for-duty data were acquired, and partly on the road, where the drivers sleepiness was assessed. A series of four measurements were conducted over a 24-h period during different stages of sleepiness. Two separate analyses were performed; a variance analysis and a feature selection followed by classification analysis. In the first analysis it was found that the SWP and several eye movement features involving anti-saccades, pro-saccades, smooth pursuit, pupillometry and fixation stability varied significantly with different stages of sleep deprivation. In the second analysis, a feature set was determined based on floating forward selection. The correlation coefficient between a linear combination of the acquired features and subjective sleepiness (Karolinska sleepiness scale, KSS) was found to be R = 0.73 and the correct classification rate of drivers who reached high levels of sleepiness (KSS ⩾ 8) in the subsequent driving session was 82.4% (sensitivity = 80.0%, specificity = 84.2% and AUC = 0.86). Future improvements of a fit-for-duty test should focus on how to account for individual differences and situational/contextual factors in the test, and whether it is possible to maintain high sensitive/specificity with a shorter test that can be used in a real-life environment, e.g. on professional drivers.     

Inferring price elasticities of car use and moral costs of driving without a licence

The article develops a model which makes it possible to infer drivers’ perceived extra costs per km of driving without a license and the moral costs of doing so. Furthermore, it gives estimates of the ratios between responses to car license suspension in different time perspectives. The calculations are carried out using data over car holders’ willingness to pay for not losing their driving license for 12 months and 24 months, their yearly driving distance and variable car usage costs. The elasticity ratios estimated here are compared with previous studies of short-term and long-term elasticities of car usage with respect to car usage costs.     

Fast computing and approximate fuel consumption modeling for Internal Combustion Engine passenger cars

This article presents a fuel consumption model, SEFUM (Semi Empirical Fuel Use Modeling), and its comparison with three models from the literature on a 600 km experimental database. This model is easy to calibrate with only a few required parameters that are provided by car manufacturers. The test database has been built from 21 drivers who drove in two conditions (normal and ecodriving) on a 15 km trip. For the model evaluation, three indicators have been selected: instantaneous fuel use root mean square error, cumulated error and computation time in order to evaluate the accuracy both in cumulated and instantaneous fuel use and to estimate computation time of each model. Results tend to prove that the model is able to compute rapidly (maximum of 1500 simulated kilometers under Matlab) in comparison to all other models while ensuring a high accuracy and precision for cumulated and instantaneous fuel use.     

Characteristics of gaseous and particulate pollutants exhaust from logistics transportation vehicle on real-world conditions

On-road vehicle tests of three heavy duty diesel trucks were conducted by a portable emission measurement system (PEMS) in Chengdu, China. SEMTECH-ECOSTAR provided by Sensors Inc. was employed to detect gaseous emissions and MI2, an emissions measuring instrument powered by the Pegasor Particulate Sensor (PPS) was used to detect particulate emissions during the tests. The impacts of speed, acceleration and engine load on emissions were analyzed. The average nitrogen oxides (NOx) emission factors of the heavy duty diesel truck (HDDT), medium-duty diesel truck (MDDT), light duty diesel truck (LDDT) were 7.29, 5.29 and 5.53 g/km. The particulate emission factors were 0.60, 0.30 and 0.14 g/km respectively, higher than the similar reported in the previous studies. Both gaseous and particulate emission exhibit significant correlations with the change in vehicle speed, acceleration and power demand. The highest emission was generally in high VSPs and higher loads. High engine load caused by aggressive driving was the main factor of high emissions for the vehicles on real-world conditions.