Karthik K Srinivasan

The introduction of mobile application-based ride hailing services represents a convergence between technologies, supply of vehicles, and demand in near real time. There is growing interest in quantifying the demand for such services from regulatory, operational, and system evaluation perspectives. Several studies model the decision to adopt ride hailing and the extent of the use of ride hailing, either separately or by bundling them into a single choice dimension, disregarding potential endogeneity between these decisions. Unlike developed countries, the literature is sparser for ride hailing in developing countries, where the demand may differ considerably because of differences in vehicle ownership, and availability and patronage of many transit and intermediate public transport (IPT) modes (the shared modes carrying 40% shares in some cases). This study aims to bridge these gaps in the literature by investigating three interrelated choice dimensions among workers in Chennai city: consideration of IPT modes, the adoption of ride hailing services and the subsequent usage intensity of ride hailing services. The main factors influencing these decisions are identified by estimating a trivariate probit model. The results indicate that sociodemographic and locational characteristics and the availability of IPT modes influence ride hailing adoption, whereas work-related constraints and perception of other modes affect its frequency. Work and non-work characteristics affect both the dimensions of ride hailing. Further, endogeneity is observed between ride hailing and IPT adoption after controlling for these variables, whereas evidence of endogeneity is absent among other dimensions. Mainly, the model separates the effect of the exogenous influences on the usage frequency level from their effect on the adoption of ride hailing services.

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.

Contemporary models consider choice sets to be either fully deterministic or fully probabilistic. Deterministic choice set models do not account for stochasticity in the choice set formation, whereas probabilistic choice set models fail to recognize that exclusion and inclusion can be deterministic for some alternatives and individuals and yet random for others. A more general scenario is, therefore, where some alternatives are deterministically included or excluded and others probabilistically included. This paper proposes a richer framework that combines the features of both deterministic and probabilistic choice set models and explicitly allows an alternative to be deterministically included, deterministically excluded, or probabilistically considered in the choice set. This framework is better than the conventional models in four aspects: (a) the factors influencing consideration type are explicitly and parametrically analyzed instead of assumption as 0 or 1; (b) the specification can disentangle factors that affect the inclusion outcome from the type of consideration; and (c) the specification also permits differential sensitivity to factors in conditional choice probability among those who consider an alternative deterministically versus probabilistically. The partially probabilistic choice set model, a special case of the proposed generalized framework, developed using empirical data collected from working commuters in Chennai city, is benchmarked against the fully probabilistic choice set models. The results show that the former had improved goodness-of-fit, realistic consideration probability estimates, and better predictability of mode shares than the latter. Relevant policies have been evaluated by identifying the appropriate target segments at both the consideration and choice stages using the proposed model.

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.

Conventional and contemporary models of travel choice make the restrictive assumption of homogeneity in decision rules. Recent literature has shown empirical evidence for potential heterogeneity in decision rules with regard to utility maximization and regret minimization. Notwithstanding these advances in modeling decision rules, behavioral understanding in the differences in these alternative decision rule segments has not been sufficiently understood. Moreover, the factors which influence the choice of these decision rules have not received significant attention. This study proposes a framework which considers decision makers to have both utility maximizing and regret minimizing tendencies. The variation in these tendencies across decision makers renders the framework heterogeneous. A heterogeneous decision rule model is developed assuming the decision rule adopted to be a latent construct. The study characterizes the regret minimizing and utility maximizing segments based on average values of the segmental attributes. The empirical findings show evidence to confirm that utility maximizers tend to be predominantly captive to personal vehicle usage while regret minimizers might be non-captive to any particular mode. The nature and extent of influence of factors affecting the choice of decision rule is also examined.

Ride-hailing services have grown in cities around the world. There are, however, few studies and even fewer publicly available data sources that provide a basis to understand and quantify changes in ride-hailing usage over time. Ride-hailing use may change over time because of socio-demographic shifts, economic and technological changes, and service attribute enhancements, as well as changes in unobserved attributes such as attitudes and perceptions, lifestyle preferences, technology savviness, and social influences. It is important to quantify the effects of these different forces on ride-hailing frequency so that robust forecasts of ride-hailing use can be developed. This paper uses repeated cross-sectional data collected in 2015 and 2017 in the Puget Sound region to analyze the differential effects of socio-demographic variables on the evolution of ride-hailing adoption and usage. By doing so, the study is able to isolate and quantify the pure effect of the passage of time on adoption of ride-hailing services. A joint binary probit-ordered probit model is estimated on the pooled dataset to explicitly account for sample-selection differences between the 2015 and 2017 surveys that may affect estimates of ride-hailing adoption in the two years. Model estimation results are used to compute average treatment effects of different variables on ride-hailing usage over time. It is found that the effects of most demographic variables on individuals’ propensity to use ride-hailing are softening over time, leading to reduced differences in ride-hailing use among market segments. This suggests that there is a “democratization” of ride-hailing services over time.

Sensitivity analyses are important parts of both studying complex systems and measuring the variation in input parameters on the response. They are useful to decision makers for understanding the robustness of the optimal solution that they are to adapt to variations of the parameters of the problem. The sensitivity of the optimal solution of a project-level pavement management problem is analyzed, and the robustness of the optimal solution to the interventions and the timing, cost, and benefit are investigated. The input parameters, which affect the optimal maintenance solution, are identified as the structural and functional condition parameters (defined in terms of deflection and roughness, respectively, at the beginning of the analysis period), traffic volume, growth rate, and discount rate. The problem of computing the optimal treatment and timing for a given budget level is modeled as a mixed integer nonlinear optimization problem and solved by using a computationally efficient network-optimization technique. The benefits are evaluated by considering the pavement performance and are quantified as the area between the performance curve and the threshold values. The optimal budget required for pavements in different structural and functional conditions as well as traffic levels is presented. The effect of initial pavement condition on the optimal maintenance actions as well as their timings is studied. The result of the sensitivity analysis showed that the cumulative standard axle loads and traffic growth rate have a significant effect on the selection and timing of rehabilitation and preventive maintenance actions. The effect of the discount rate on the maintenance management decisions is also presented.

This paper focuses on systematic behavioral heterogeneity in mode choice decisions of working population in Chennai city. The sources of heterogeneity that are investigated include (a) variation in choice set across decision makers, (b) differences in innate preferences and responsiveness to explanatory factors due to variations in their degree of captivity to non-personal modes and number of co-passengers during work commute. Existing studies that account for such systematic differences among decision-maker in a developing country context capture only the preference heterogeneity, impose unrealistic restrictions on the utility of non-personal modes (say for e.g. for joint (copassengers > 0) trips) and specify choice sets to be fixed and identical across decision-makers. Addressing these issues, the main objectives of the study were to capture the effect of factors influencing perceived availability of alternatives in the choice set; behavioral comparison of alternate choice set representations namely: fixed choice set, choice set with explicit specification of unavailability of alternatives, partial and probabilistic choice set; investigate potential heterogeneity in innate preference to alternatives and responsiveness to explanatory factors due to difference in captivity status and the presence of co-passengers; and examine whether and how these heterogeneity effects differ based on alternative choice set representations. The effect of factors influencing the perceived availability of alternatives is captured using a set of binary logit models. Unlike many studies, where captivity is represented by a binary variable, behavioral differences across three levels of captivity (captive by vehicle ownership, captive by driving knowledge, and semi-captive) are investigated. The results from the empirical analysis show that disregarding preference or response heterogeneity, when present, can lead to poorer goodness-of-fit in models. It also shows that partial and probabilistic choice set representation is more behaviorally consistent than explicit specification of alternative unavailability, which in turn is better than assuming choice set to be identical across decision-makers.