A lane-change trajectory model from drivers’ vision view

Car-following and Lane-changing are two fundamental tasks during driving. While many car-following models can be applied, relatively, only a few lane-changing models have been developed. Classical lane-changing models mainly focus on drivers’ lane selection and gap acceptance behaviors, but very limited research has paid attention to formulating detailed lane-changing trajectories. This research aims to fill the gap by proposing a lane-changing trajectory model, which is built directly from drivers’ vision view, to model detailed lane-changing trajectories. A large amount of data of reference angles, defined as the angle changes between the drivers’ vision angle and left or right lane line, were first extracted from the videos recorded by the vehicle traveling data recorders (VTDRs) installed in 11 taxies. A comprehensive data analysis indicates that same drivers show similarity of their daily lane-changing habit but with variety, and different drivers’ lane-change trajectory data show different lane-change “personality” including aggressive or non-aggressive behaviors. Based on these findings, this paper then proposed a hyperbolic tangent lane-change trajectory model to describe drivers’ detailed lane-change trajectories. The model is verified using both real data and simulation. The results show the proposed lane-change trajectory model can successfully describe drivers’ lane-changing trajectories. More importantly, some parameters in the model are directly associated to drivers’ driving characteristics during lane-change. With this unique feature, the proposed model can generate driver-specific lane-change trajectories. Such improvement could contribute to the future development of Advanced Driver Assistance Systems (ADAS).     

Estimation of an integrated driving behavior model

This paper presents the methodology and results of estimation of an integrated driving behavior model that attempts to integrate various driving decisions. The model explains lane changing and acceleration decisions jointly and so, captures inter-dependencies between these behaviors and represents drivers’ planning capabilities. It introduces new models that capture drivers’ choice of a target gap that they intend to use in order to change lanes, and acceleration models that capture drivers’ behavior to facilitate the completion of a desired lane change using the target gap.The parameters of all components of the model are estimated simultaneously with the maximum likelihood method and using detailed vehicle trajectory data collected in a freeway section in Arlington, Virginia. The estimation results are presented and discussed in detail.     

Analysis of parking cruising behaviour and parking location choice

ABSTRACT

This paper explores car drivers’ cruising behaviour and location choice for curb parking in areas with insufficient parking space based on a survey of car drivers in Beijing, China. Preliminary analysis of the data show that car drivers’ cruising behaviour is closely related to their parking duration and parking location. A multinomial probit (MNP) model is used to analyse cruising behaviour and the results show that the closer to the destination car drivers are, the more likely they choose to park on the curb. The adjacent locations are the basis of car drivers’ sequential parking decisions at different locations. The research results provide a better understanding of cruising behaviour for parking and recommendations for reducing cruising for parking. The provision of parking information can help regulate the parking demand distribution.     

Users’ perceptions and willingness to pay in interurban toll roads: identifying differences across regions from a nationwide survey in Spain

Users’ acceptability is considered one of the key drivers for the successful implementation of transport policy measures. This is especially crucial in the case of toll roads since they are financed through drivers’ contributions. Previous literature in this field has mainly focused on measuring users’ attitudes towards urban congestion pricing strategies. However limited research has been developed concerning interurban toll roads. Previous research shows that socioeconomic variables are not conclusive to explain users’ perceptions towards tolls. By contrast, other drivers such as regional differences seem to play a more important role, especially when charging conditions within the same nation greatly vary across regions. This paper analyzes regional differences in users’ attitudes within an asymmetrical distribution of the toll road network across regions. Based on a nationwide survey conducted to road users in interurban toll roads in Spain, we develop both a binomial logit and a censored regression (tobit) model to explore drivers’ perceptions and willingness to pay. The research concludes that users from regions with a more extensive tolled network generally show a more negative attitude towards charges, but not necessarily a lower willingness to pay. The paper also points out that an asymmetrical distribution of toll roads across regions may result in negative perceptions among those users perceiving to be unfairly treated when compared to citizens in other regions.     

Modeling taxi drivers’ decisions for improving airport ground access: John F. Kennedy airport case

Taxi service is an important component of airport ground access, which affects the economic competitiveness of an airport and its potential positive impact on the surrounding region. Airports across the globe experience both taxi shortages and excesses due to various factors such as the airport’s proximity to the city center, timing and frequency of flights, and the fare structure. Since taxi drivers are independent entities whose decisions affect the taxi supply at airports, it is important to understand taxi drivers’ decision mechanisms in order to suggest policies and to maintain taxi demand and supply equilibrium at the airports. In this paper, New York City (NYC) taxi drivers’ decisions about airport pick-ups or cruising for customers at the end of each trip is modeled using logistic regression based on a large taxi GPS dataset. The presented approach helps to quantify the potential impacts of parameters and to rank their influence for policy recommendations. The results reveal that spatial variables (mainly related to proximity) have the highest impact on taxi drivers’ airport pickup decisions, followed by temporal, environmental and driver-shift related variables. Along with supplementary information from unstructured taxi driver interviews, the model results are used to suggest policies for the improvement of John F. Kennedy (JFK) airport’s ground access and passenger satisfaction, i.e. the implementation of taxi driver frequent airport server punch cards and a time-specific ride share program.     

Eco-driving behaviors of electric vehicle users: A survey study

Previous research has shown that electric vehicle (EV) users could behave differently compared to internal combustion engine vehicle (ICEV) drivers due to their consciousness or practices of eco-driving, but very limited research has fully investigated this assumption. This research explores this topic through investigating EV drivers’ eco-driving behaviors and motivations. We first conducted a questionnaire survey on EV drivers’ driving behavior and some hypothetical decisions of their driving. It indicates various characteristics between EV and ICEV commuters, including self-reported daily driving habits, preferences of route choices, tradeoff between travel time and energy saving, and adoption of in-vehicle display (IVD) technologies. Then, through statistical analysis with Fisher’s exact test and Mann-Whitney U test, this research reveals that, compared to ICEV drivers, EV drivers possess significantly calmer driving maneuvers and more fuel-efficient driving habits such as trip chaining. The survey data also show that EV drivers are much more willing to save energy in compensation of travel time. Furthermore, the survey data indicate that EV drivers are more willing to adopt eco-friendly IVD technologies. All these findings are expected to improve the understanding of some unique behavior found in EV drivers.     

A discounted recursive logit model for dynamic gridlock network analysis

Emerging sensing technologies such as probe vehicles equipped with Global Positioning System (GPS) devices on board provide us real-time vehicle trajectories. They are helpful for the understanding of the cases that are significant but difficult to observe because of the infrequency, such as gridlock networks. On the premise of this type of emerging technology, this paper propose a sequential route choice model that describes route choice behavior, both in ordinary networks, where drivers acquire spatial knowledge of networks through their experiences, and in extraordinary networks, which are situations that drivers rarely experience, and applicable to real-time traffic simulations. In extraordinary networks, drivers do not have any experience or appropriate information. In such a context, drivers have little spatial knowledge of networks and choose routes based on dynamic decision making, which is sequential and somewhat forward-looking. In order to model these decision-making dynamics, we propose a discounted recursive logit model, which is a sequential route choice model with the discount factor of expected future utility. Through illustrative examples, we show that the discount factor reflects drivers’ decision-making dynamics, and myopic decisions can confound the network congestion level. We also estimate the parameters of the proposed model using a probe taxis’ trajectory data collected on March 4, 2011 and on March 11, 2011, when the Great East Japan Earthquake occurred in the Tokyo Metropolitan area. The results show that the discount factor has a lower value in gridlock networks than in ordinary networks.     

Analysis of yellow-light running at signalized intersections using high-resolution traffic data

Many accidents occurring at signalized intersections are closely related to drivers’ decisions of running through intersections during yellow light, i.e., yellow-light running (YLR). Therefore it is important to understand the relationships between YLR and the factors which contribute to drivers’ decision of YLR. This requires collecting a large amount of YLR cases. However, existing data collection method, which mainly relies on video cameras, has difficulties to collect a large amount of YLR data. In this research, we propose a method to study drivers’ YLR behaviors using high-resolution event-based data from signal control systems. We used 8 months’ high-resolution data collected by two stop-bar detectors at a signalized intersection located in Minnesota and identified over 30,000 YLR cases. To identify the possible reasons for drivers’ decision of YLR, this research further categorized the YLR cases into four types: “in should-go zone”, “in should-stop zone”, “in dilemma zone”, and “in optional zone” according to the driver’s location when signal turns to yellow. Statistical analysis indicates that the mean values of approaching speed and acceleration rate are significantly different for different types of YLR. We also show that there were about 10% of YLR drivers who cannot run through intersection before traffic light turns to red. Furthermore, based on a strong correlation between hourly traffic volume and number of YLR events, this research developed a regression model that can be used to predict the number of YLR events based on hourly flow rate. This research also showed that snowing weather conditions cause more YLR events.     

Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study

Although car-following behavior is the core component of microscopic traffic simulation, intelligent transportation systems, and advanced driver assistance systems, the adequacy of the existing car-following models for Chinese drivers has not been investigated with real-world data yet. To address this gap, five representative car-following models were calibrated and evaluated for Shanghai drivers, using 2100 urban-expressway car-following periods extracted from the 161,055 km of driving data collected in the Shanghai Naturalistic Driving Study (SH-NDS). The models were calibrated for each of the 42 subject drivers, and their capabilities of predicting the drivers’ car-following behavior were evaluated.The results show that the intelligent driver model (IDM) has good transferability to model traffic situations not presented in calibration, and it performs best among the evaluated models. Compared to the Wiedemann 99 model used by VISSIM®, the IDM is easier to calibrate and demonstrates a better and more stable performance. These advantages justify its suitability for microscopic traffic simulation tools in Shanghai and likely in other regions of China. Additionally, considerable behavioral differences among different drivers were found, which demonstrates a need for archetypes of a variety of drivers to build a traffic mix in simulation. By comparing calibrated and observed values of the IDM parameters, this study found that (1) interpretable calibrated model parameters are linked with corresponding observable parameters in real world, but they are not necessarily numerically equivalent; and (2) parameters that can be measured in reality also need to be calibrated if better trajectory reproducing capability are to be achieved.     

The effect of travel time variability on route choice decision: a generalized linear mixed model based analysis

The effect of travel time variability (TTV) on route choice behavior is explored in this study. A stated preference survey is conducted to collect behavioral data on Shanghai drivers’ choice between a slow but stable route and a fast but unreliable route. Travel time and TTV are respectively measured by mean and standard deviation of random travel time. The generalized linear mixed model (GLMM) is applied to quantify trade-offs between travel time and TTV. The GLMM based route choice model effectively accounts for correlations among repeated observations from the same respondent, and captures heterogeneity in drivers’ values of TTV. Model estimation results show that, female drivers and drivers with rich driving experience are less likely to choose a route with high TTV; smaller expected travel time of a route increase the probability of its being chosen; all drivers have intrinsic preference for a route with smaller expected travel time, but the degree of preference may vary within the population; TTV on average has negative effects on route choice decision, but a small portion of drivers are risk-prone to choose a fast but unreliable route despite high TTV.     

Estimating the value of risk reductions for car drivers when pedestrians are involved: a case study in Spain

We estimated the benefits associated with reducing fatal and severe injuries from traffic accidents using a stated choice experiment where choice situations were generated through a statistically efficient design. Specifically, the risk variables were defined as the expected annual number of vehicle car-users that suffered their death or were severely injured in a traffic accident. In addition, and differing from previous research, the number of pedestrians that died or were severely injured in traffic accidents per year was also included as a risk attribute in the choice experiment, to attempt at measuring drivers’ willingness to pay to reduce the risk of hitting pedestrians in a crash. The empirical setting was a choice of route for a particular trip that a sample of car drivers periodically undertakes in Tenerife, Spain. Models were estimated accounting for random taste heterogeneity and pseudo-panel data correlation. The median of the distribution of simulated parameters was used to obtain a representative measure for the monetary valuation of risk reductions. We found that the ratio between the values of reducing the risk of suffering a serious injury and that of reducing a fatality was approximately 18 %. Further, and quite novel, we also found that the value of reducing a pedestrian fatality was 39 % of the value of reducing a car occupant fatality.     

Driving with intelligent speed adaptation: Final results of the Belgian ISA-trial

In October 2002 the first ISA-trial in Belgium was started in Ghent. Thirty-four cars and three buses were equipped with the “active accelerator pedal”. In this system a resistance in the accelerator is activated when the driver attempts to exceed the speed limit. If necessary, the driver can overrule the system. The main research goals of the trial in Ghent were to evaluate the effects of ISA on speed-change, traffic safety, drivers’ attitude, behaviour and drivers’ acceptance. To study these effects of the ISA-system both surveys and logged speed data were analyzed. In the surveys drivers noticed that the pedal assisted them well in upholding the speed limits and that the system increased driving comfort. Most important drawbacks were technical issues. Data analysis shows a reduction in the amount of speeding due to the ISA-system. There is however still a large remaining percentage of distance speeding, especially in low speed zones. Differences between drivers are large. For some drivers speeding even increases despite activation of the system. For less frequent speeders average driving speed almost always increases and for more frequent speeders average speed tends to decrease. With the system, less frequent speeders tend to accelerate faster towards the speed limit and drive exactly at the speed limit instead of safely below, which causes average speeds to go up.     

Effects of crash warning systems on rear-end crash avoidance behavior under fog conditions

Reduced visibility conditions increase both the probability of rear-end crash occurrences and their severity. Crash warning systems that employ data from connected vehicles have potential to improve vehicle safety by assisting drivers to be aware of the imminent situations ahead in advance and then taking timely crash avoidance action(s). This study provides a driving simulator study to evaluate the effectiveness of the Head-up Display warning system and the audio warning system on drivers’ crash avoidance performance when the leading vehicle makes an emergency stop under fog conditions. Drivers’ throttle release time, brake transition time, perception response time, brake reaction time, minimum modified time-to-collision, and maximum brake pedal pressure are assessed for the analysis. According to the results, the crash warning system can help decrease drivers’ reaction time and reduce the probability of rear-end crashes. In addition, the effects of fog level and drivers’ characteristics including gender and age are also investigated in this study. The findings of this study are helpful to car manufacturers in designing rear-end crash warning systems that enhance the effectiveness of the system’s application under fog conditions.     

Analysis of asymmetric driving behavior using a self-learning approach

This paper presents a self-learning Support Vector Regression (SVR) approach to investigate the asymmetric characteristic in car-following and its impacts on traffic flow evolution. At the microscopic level, we find that the intensity difference between acceleration and deceleration will lead to a ‘neutral line’, which separates the speed-space diagram into acceleration and deceleration dominant areas. This property is then used to discuss the characteristics and magnitudes of microscopic hysteresis in stop-and-go traffic. At the macroscopic level, according to the distribution of neutral lines for heterogeneous drivers, different congestion propagation patterns are reproduced and found to be consistent with Newell’s car following theory. The connection between the asymmetric driving behavior and macroscopic hysteresis in the flow-density diagram is also analyzed and their magnitudes are shown to be positively related.     

Modeling the charging and route choice behavior of BEV drivers

Due to the limited cruising range of battery electric vehicle (BEV), BEV drivers show obvious difference in travel behavior from gasoline vehicle (GV) drivers. To analyze BEV drivers’ charging and route choice behaviors, and extract the differences between BEV and GV drivers’ travel behavior, two multinomial logit-based and two nested logit-based models are proposed in this study based on a stated preference survey. The nested structure consists of two levels: the upper level represents the charging decision, and the lower level shows the route choices corresponding to the charging and no-charging situations respectively. The estimated results demonstrate that the nested structure is more appropriate than the multinomial structure. Meanwhile, it is observed that the initial state of charge (SOC) at origin of BEV is the most important factor that affects the decision of charging or not, and the SOC at destination becomes an important impact factor affecting BEV drivers’ route choice behavior. As for the route choice behavior when BEV has charging demand, the charging station attributes such as charging time and charging station’s location have significant influences on BEV drivers’ decision-making process. The results also show that BEV drivers incline to choose the routes with charging station having less charging time, being closer to origin and consistent with travel direction. Finally, based on the proposed models, a series of numerical analysis has been conducted to verify the effect of range anxiety on BEV charging and route choice behavior and to reveal the variation of comfortable initial SOC at origin with travel distance. Meanwhile, the effects of charging time and distance from origin to charging station also have been discussed.     

Intelligent transportation system and traffic safety – drivers perception and acceptance of electronic speed checkers

Intelligent Transportation System (ITS) can play an important role in reducing risks and increasing traffic safety. Discussion as to whether a technological approach or a behavioral approach is the right way to achieve a safer traffic environment forms a point of departure for this paper. On the one hand, there are the technicians who emphasize technology as the way towards safer traffic. Behaviorists, on the other hand, view the drivers’ behavior as fundamental and argue that education and incentive-oriented policies are essential in order to influence the driver and therefore increase traffic safety. Independent of the approach advocated a successful outcome of either a technological improvement, or an information campaign, has to be based on a high level of acceptance among potential users. In order to increase traffic safety, it is therefore essential to recognize driver motivation and attitudes. In this paper we focus on drivers’ attitudes towards risk, traffic safety and safety measures. A study of drivers’ attitudes and acceptance of an electronic device for speed checking (which the drivers tested for nine months) indicated a high acceptance level. The drivers perceived that they had both become more aware of traffic regulations and behaved in accordance with safety regulations.     

Route Choice Behaviour of Freeway Travellers Under Real-time Traffic Information Provision – Application of the Best Route and the Habitual Route Choice Mechanisms

Abstract

This paper investigates route choice behaviour on freeways between Taipei and Taichung in Taiwan under the provision of real-time traffic information. Two types of route choice selection rules (the best-route and habitual-route) are analysed using ordered probit models to identify the major influences on freeway travellers’ route choice behaviour. The level of service associated with each route is defined as a generalised cost saving (GCS) and specified non-linearly with a threshold inherent to travellers. The marginal (dis)utility thresholds in the ‘best’ and ‘habitual’ behaviour models are identified through a goodness-of-fit grid. The results confirm that the thresholds for changing the inertia behaviour of drivers should be larger than the ones for choosing the best routes. In addition, the drivers are more likely to choose either the best or the habitual routes once the GCS are greater than the identified threshold values.     

A methodology to evaluate driving efficiency for professional drivers based on a maturity model

Over the last decade, transport companies have tried to reduce fuel consumption using efficient driving programs. In them, motorists have to apply different specific techniques while driving. Thus, to succeed in this learning process there are two key elements: the knowledge of efficient driving techniques and the drivers’ motivation. The latter is a human factor which companies usually bring about by using reward systems. In this case, having a fair evaluation mechanism is the keystone to determine goal fulfilment. This paper presents a complete methodology to evaluate driving efficiency of drivers in professional fleets. The evaluation methodology is based on a continuous process which determines the maturity of the motorist in different aspects, such as the efficiency during the start of the vehicle movement, during motion or in stop events. In addition, the evaluation methodology includes an early-classification method to establish the initial efficiency level of the individual drivers which permits an adaptation of the learning process from the beginning. A dashboard has also been developed to support the evaluation methodology. 880 professional drivers have been evaluated with this methodology. Results show that the evaluation methodology identifies drivers’ weaknesses, to be improved in successive iterations of the learning process.     

Interior noise profiles of buses in Curitiba

This study evaluates the noise level exposure of noise levels to which the bus drivers in Curitiba. Two measurements were taken inside 25 buses, the first close to the driver and the second at the back of the bus. In 23 buses the noise exposure of drivers was ‘acceptable’ according to the definition of the Occupational Health Standard of the Brazilian Ministry of Labor. On the other hand, the normalized exposure levels were all over 65 dB(A) in all cases making the work environment ‘uncomfortable’ according to the Brazilian Legislation for Ergonomics (NR-17). The year of manufacture and the location of the engine are relevant factors in determining the noise inflicted on drivers.     

Modelling parking choices considering user heterogeneity

We examine car driver’s behaviour when choosing a parking place; the alternatives available are free on-street parking, paid on-street parking and parking in an underground multi-storey car park. A mixed logit model, allowing for correlation between random taste parameters and estimated using stated choice data, is used to infer values of time, both when looking for a parking space and for accessing the final destination. Apart from the cost of parking, we found that vehicle age was a key variable when choosing where to park in Spain. We also found that the perception of the parking charge was fairly heterogeneous, depending both on the drivers’ income levels and whether or not they were local residents. Our results can be generalised for personalised policy making related with parking demand management.