Karthik K Srinivasan

Driving behaviour in mixed traffic conditions is chara-cterized by vehicle heterogeneity and lane-less move-ment. In such traffic conditions, the following response of a vehicle may be discontinuous and gets triggered when certain thresholds on relative speed and spacing with the leaders are crossed. In this context, the pre-sent study segments vehicular response into driving re-gimes using vehicle trajectory data based on relative speed and position. Acceleration models are formulat-ed by featuring driving regimes and their interactions with mixed traffic attributes. These models are used to study the differences in the following behaviour of two-wheelers and cars. The proposed models capture the asymmetric behaviour and account for differences across driving regimes, resulting in a significantly bet-ter fit and realistic representation of mixed traffic.

Mixed traffic in the cities of many developing countries is characterized by a lack of lane discipline, varying compositions of constituent vehicle types, and significant intraclass variability in static and dynamic characteristics. However, the influence of these factors on traffic flow parameters is not well understood. This study addressed the influence of lane discipline, intraclass variability, and composition on traffic flow characteristics under heterogeneous traffic conditions in Chennai, India. A microscopic traffic simulation model was calibrated and validated with field data from a four-lane divided urban arterial road in Chennai. The preliminary analysis indicated that factors such as composition, intraclass variability, and lane discipline had a statistically significant effect on stream speed. Speed-flow and speed-density relationships were developed on the basis of simulation results. These results showed a clear influence of lack of lane discipline, variability, and composition on stream speed. The influence varied depending on volume level and type of subject vehicle. The effect of composition on capacity was quantified. When two-wheelers had a predominant share, they enjoyed better performance in the absence of lane discipline. However, when cars and heavy vehicles had a significant presence, the impact of the lack of lane discipline was much smaller. The simulation model was applied to evaluate a range of traffic control measures based on vehicle type and lane. The results showed the promise of some measures based on vehicle class, namely, the exclusion of autorickshaws or autorickshaws and heavy vehicles. The findings have interesting implications for efficiency, user experience, and equity in mixed traffic.

In mixed traffic condition, varying vehicle dimensions and lack of lane discipline lead to parallel movement of vehicles in the same lane or between lanes. This results in a condition where the longitudinal response of vehicles gets affected by adjacent vehicles and their configurations. Correspondingly, the adjacent vehicle configurations are significantly influenced by the lane position of subject vehicle. To accommodate this scenario, the study formulates longitudinal acceleration models from trajectory data considering the influence of subject vehicle’s lane position along with adjacent vehicle characteristics. The response under different cases of subject vehicle’s lane position and adjacent vehicle configurations are evaluated. The statistical analysis indicates that the following behavior varies based on lane position of subject vehicle and adjacent vehicle attributes. It is also found that disregarding these attributes can produce significantly erroneous acceleration estimates. These features can improve existing following behavior models and can enhance the realism of the microscopic modeling scheme for mixed traffic conditions.

Indian traffic can be considered as mixed and heterogeneous due to the presence of various types of vehicles that operate with weak lane discipline. Consequently, vehicles can position themselves anywhere in the traffic stream depending on availability of gaps. The choice of lateral positioning is an important component in representing and characterising mixed traffic. The present study aims to develop a methodology to extract the trajectory of vehicle for heterogeneous non-lane based traffic condition. To study the movement pattern of vehicle types and to explore the vehicular behaviour and its reaction to different traffic environment, it is essential to extract the trajectory data of vehicles. Therefore, a semi-automated tool using python's graphical user interface is developed to extract the vehicle trajectory. The field data provides evidence that the trajectory of vehicles in Indian urban roads have significantly varying longitudinal and lateral components and the traffic flow characteristics of each vehicle types vary from one another. Present study analysis the variation in driving behaviour of vehicle with lateral position characteristics. It has been found that the following behaviour of vehicles varies with the lane position and the traffic parameters of each lane differ from each other.

In developing countries such as India, the traffic on roads is highly heterogeneous in nature, with vehicles of widely varying static and dynamic characteristics. This type of traffic is characterized by lack of lane discipline and free movement over the entire width of the roadway (laneless movement) based on availability of space. In such a regime of traffic flow, the phenomenon of merging of vehicles at junctions of two roads is complex and warrants further study. Merging maneuvers at T-junctions under congested traffic conditions were studied microscopically through video recordings. On the basis of these observations, models for normal and forced merging have been developed. Video data of merging maneuvers were employed to calibrate and validate the models. Such models can be used to simulate highly congested traffic flow in a realistic manner under heterogeneous traffic conditions. They can also give insight on devising better traffic control measures at such junctions.

Roads in developing countries carry mixed traffic with wide variations in static and dynamic characteristics of vehicles. The traffic flow is also generally devoid of lane discipline, with vehicles occupying any available road space ahead. In such a regime of traffic flow, the phenomena of merging of vehicles at intersections of two roads is complex, warranting further study. The merging maneuvers at T-intersections under congested traffic conditions were studied microscopically through video-recording. In congested situations, the merging vehicle attempts a complex merging maneuver to enter the main traffic stream. Two unique merging processes are commonly observed in mixed traffic: group and vehicle cover merging (these are generally not observed in countries such as US). The author is using these words first time in this study. These reflect the different types of driver behavior - merging in groups, and by taking cover of another vehicle. Probabilistic models for group and vehicle cover merging are developed that capture this unique merging behavior. Comprehensive microscopic data collection and extraction were carried out to study the merging process at T-intersection under congested conditions. Merging models were then estimated using maximum likelihood method with disaggregate data that was collected for a case study T-intersection in Chennai city, India. Such models can find applications in simulation of highly congested traffic flow in a realistic manner under mixed traffic conditions. They can also give insights on devising better traffic control measures at such intersections.

In mixed traffic condition, vehicles with varying static and dynamic characteristics share the same road space. The smaller vehicles frequently try to manoeuvre through the gaps available between larger vehicles. In this scenario, the use of conventional vehicle-following models which considers the effect of single (overlapping) leader may become inappropriate. The influence of multiple leaders and their combinations on the response of the subject vehicle is a crucial research gap identified from the literature survey. In the multiple leader cases, the follower does not merely adjust to the relative speed and spacing with the primary (overlapping) leader, but judiciously responds to subsidiary (non-overlapping) leader attributes also. Present study formulates a model structure for vehicle-following manoeuvre in mixed traffic by incorporating the multiple leader dynamic variables. The spatial orientation of multiple leaders along with vehicle types and dynamic variables are found to have a substantial role, thus resulting in realistic representation of mixed traffic. These characteristics have the potential to enhance existing following behaviour models and improve the realism of microscopic modelling in mixed traffic conditions. Simulation models that integrate these attributes could find practical applications in more accurate assessments of traffic management and operational strategies.

Advanced Traveler Information Systems (ATIS) assists travelers in making informed travel decisions, thereby helping them to avoid delays and hazardous situations. This improves the overall traffic safety and performance of the network. Even if the information system provides accurate and reliable information, if the travellers do not perceive the system quality positively, it might lead to less compliance and consultation by travellers. In this study, we identified the factors that influence the perceieved accuracy of traveler information delivered through Variable Message Sign (VMS) boards. A binary logit model of perceived accuracy is developed using data collected by revealed preference questionnaire survey. The results indicate that the socioeconomic characteristics and attributes related to traveler information play a significant role in developing a positive perception about information accuracy.

This paper models the following behaviour of vehicles in mixed traffic using trajectory data. New models for the longitudinal acceleration of following vehicles are developed based on vehicle type differences and their asymmetric interactions. Two new variables capture the effect of lack of lane-discipline: lateral offset and local area concentration. These variables not only affect the follower's acceleration but also the spacing and relative speed. The following behaviour also varies across gap-narrowing and gap-widening regimes. The proposed models considerably improve the goodness-of-fit measures compared to the base models. Such models are believed to add value to microscopic simulation models of mixed traffic by enhancing their realism.