Browsing by Author "Marisamynathan, Sankaran"

Now showing 1 - 6 of 6
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    Development of freight travel demand model with characteristics of vehicle tour activities
    (Elsevier Ltd, 2020-11-01) Venkadavarahan M.; Raj, Celestin Thivya; Marisamynathan, Sankaran
    The actual movements of freight vehicles demonstrate comprehensive tour activities instead of a single trip. Freight vehicle tours activity represent trip chaining relationship between shippers, receivers, and carriers as a journey of tours. In India, identifying and modeling tour activity is complex because disaggregated freight tour activity data are generally not available to access. Hence, there is a need to identify the most suitable and sufficient modeling framework, which explains the non-linear behavior and complexity of tour-based freight travel demand. Thus, the objectives of this study are to identify the driver, vehicle, and journey characteristics of freight vehicle movement and their tour activities using the conventional non-linear and soft computing methods to estimate the number of tours and to identify the most suitable and effective modeling method. The questionnaire form designed, and study locations were selected in Tiruchirappalli, India. The selected survey locations were identified based on the availability of fright drivers who were making tours. An extensive face to face data collection process was performed, and a total of 600 drivers' data were collected from survey. A detailed description of the collected data was obtained through descriptive analysis. Next, significant relationship between characteristics of freight vehicle drivers and their tour activities was identified based on various correlation tests. Further, a non-linear regression method was adopted for explaining non-linear behavior and complexity of tour-based freight travel demand. Followed by soft computing models using Support-vector machines (SVM), k-Nearest Neighbors (k-NN), and Artificial Neuron Network (ANN) algorithms were developed to predict tour-based freight travel demand, which performed better than conventional non-linear model. Among all, ANN model outperformed and explained the variations with better accuracy. Finally, the outcome of this study will be helpful to transport planners and researchers for understanding the characteristics of freight vehicle tour activity and estimating tour-based freight travel demand.
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    Development of urban freight trip generation models concerning establishment classification process for a developing country
    (KeAi Communications Co., 2021-08-19) Venkadavarahan, Marimuthu; Marisamynathan, Sankaran
    The objective of this study is to develop the Freight Trip Generation (FTG) models concerning establishment classification process for estimating freight trip activity. Establishment Based Freight Survey (EBFS) was performed to collect various supply chain variables involved in the urban freight system. Extensive data of 647 samples were collected from Tiruchirappalli city, India. Economic activity analysis shows that pure receiver and intermediate establishments do not follow the uniform freight trip activity among the industrial segments. Hence, a suitable methodology for the establishment classification process is developed using traditional and machine learning (ML) techniques to classify the establishment into an intermediate and pure receiver before estimating the freight trip activity. Supply chain variables like employment, FTA, industrial segment, mode of the commercial vehicle, type of delivery, number of suppliers, and gross floor area play a crucial role in the establishment classification process. Then, employment-based FTG models are developed with the classified data using linear and non-linear functional forms for each industrial segment. Finally, it is observed that FTG models result enhanced, when the establishment classification process is performed as an initial stage, with lower error and accurate estimation of urban freight trip activity.
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    Investigating the contributory factors influencing speeding behavior among long-haul truck drivers traveling across India: Insights from binary logit and machine learning techniques
    (KeAi Communications Co., 2024-01-30) Shandhana Rashmi, Balamurugan; Marisamynathan, Sankaran
    Speeding is one of the most common aberrant driving behaviors among the driving population. Although research on speeding behavior among drivers has increased over the decades, little is known about the motivating factors associated with speeding behavior among long-haul truck drivers (LHTDs), especially in developing nations like India. This study aims to develop a prediction model for speeding behavior and to identify the contributory factors and their influential patterns underlying speeding behavior among LHTDs in India. A cross-sectional study was conducted among LHTDs in Salem City, Tamil Nadu, India. The data were collected through face-to-face interviews using a questionnaire encompassing socio-demographic, work, vehicle, health-related lifestyle, and speeding-related characteristics. A total of 756 valid samples were collected and utilized for analysis purposes. While conventional statistical methods like binary logit technique lacked prediction capabilities, machine learning (ML) algorithms including decision tree (DT), random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost) were employed to model speeding behavior among LHTDs. The analysis results showed that RF demonstrated superior performance in predicting speeding behavior over other competing algorithms with accuracy (0.80), F1 score (0.77), and AUROC (0.81). From the befitting RF model, the importance of factors contributing to speeding behavior among LHTDs was determined through the variable importance plot. Pressured delivery of goods, sleeping duration per day, age of truck, size of truck, monthly income, driving experience, driving duration per day, and age of the driver were identified as the eight topmost critical factors contributing to speeding behavior among LHTDs. Based on the developed RF model, the hidden relationships behind identified critical factors in relation to the speeding behavior were investigated using partial dependence plots (PDPs). The outcomes of this research will be useful for road safety authorities and Indian trucking industries to frame suitable policies and to introduce effective strategies for mitigating speeding behavior among LHTDs to promote road safety
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    Mode shift behaviour and user willingness to adopt the electric two-wheeler: A study based on Indian road user preferences
    (KeAi Communications Co., 2022-04-02) Murugan, Manivel; Marisamynathan, Sankaran
    As per statistics, two-wheeler (TW) alone shares the highest number of vehicle registrations in India, which develops the various transportation-related issues such as traffic conflicts, congestions, and pollutions. Electric two-wheelers (E-TW) are a better alternative to conventional two-wheelers because of their significant advantage in mitigating environmental impacts. But E-TWs are less attractive among road users due to unawareness of the benefits of E-TW. In addition, the traditional methods are less accurate in predicting users' mode choice behavior because of their limitations. Therefore, there is a need to conduct a study to understand the road user's willingness to adopt E-TW and find a suitable method for predicting mode choice behavior accurately. This study analyzes the Indian road users encouraging and discouraging factors to adopt E-TW and investigates the application of non-traditional models for estimating mode shift behaviour towards E-TW. Based on the literature review and expert opinion, a detailed questionnaire form was framed, and a total of 522 samples were collected from four states of India. The data findings show that Indian road users prefer TW compared to public transport, private four-wheeler, paratransit, and non-motorized transport because of its easy to ride, low maintenance, fast and convenient travel nature. The environmental concern of reducing air pollution and lower vehicle operating costs are significant factors that encourage E-TW adoption. However, the non-availability of charging infrastructure, lower speed, higher initial purchase cost, and lack of awareness about EVs are the significant discouraging factors in adopting E-TW in India. Further, Machine Learning (ML) methods were adopted to predict the mode shift behaviour from the fuel based TW to E-TW, and the results were compared with the Binary Logit (BL) method. The model results indicated that Support vector machine predicted the mode shift behavior with the highest accuracy rate compared with other methods such as Artificial Neural Network, K-Nearest Neighbor, Random Forest, and BL. The outcome of this study would help the transportation planner, EV manufacturers, researchers, and policymakers to understand the Indian user's preference to adopt E-TW.
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    Pedestrian delay models for compliant & non-compliant behaviour at signalized midblock crosswalks under mixed traffic conditions
    (Elsevier B.V., 2023-08-28) Manthirikul, Sandeep; Jain, Udit; Marisamynathan, Sankaran
    The present study aimed to propose new pedestrian delay models for Signalized Midblock Crosswalks (SMC) for mixed traffic conditions. A detailed study of the literature revealed that most of the existing pedestrian delay models were developed for signalized intersections. Thus, the need for the study was established and data were collected at eight SMC in Hyderabad, one of the most densely populated metropolitan cities in India, using video-graphic technique. Two delay models were developed based on the compliance behaviour and non-compliance behaviour of pedestrians. Both models have two components i.e., waiting delay and crossing delay where the latter has two subset components i.e., frictional delay and pedestrian-vehicle interaction delay. The bidirectional effect (pedestrian-pedestrian interaction while crossing the road) of pedestrians was addressed as frictional delay while the non-compliance behaviour by pedestrians and vehicles was addressed as pedestrian-vehicle interaction delay in the present models. The waiting delay component was defined by modifying the Webster delay model for non-uniform pedestrian arrivals. The proposed delay models yielded an error of 5% and 7% for compliance behaviour model and non-compliance behaviour model respectively. The proposed models can be used for optimizing the signal timings and defining Level of Service (LOS) of facilities.
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    Pedestrian safety evaluation of signalized intersections using surrogate safety measures
    (VGTU, 2020-03-04) Marisamynathan, Sankaran; Vedagiri, Perumal
    The large proportions of pedestrian fatalities led researchers to make the improvements of pedestrian safety at intersections. Thus, this paper proposes a methodology to evaluate crosswalk safety at signalized intersections using Surrogate Safety Measures (SSM) under mixed traffic conditions. The required pedestrian, traffic, and geometric data were extracted based on the videographic survey conducted at signalized intersections in Mumbai (India). Post Encroachment Time (PET) for each pedestrian were segregated into three categories for estimating pedestrian–vehicle interactions and Cumulative Frequency Distribution (CDF) was plotted to calculate the threshold values for each interaction severity level. The Cumulative Logistic Regression (CLR) model was developed to predict the pedestrian mean PET values in the crosswalk at signalized intersections. The proposed model was validated with a new signalized intersection and the results were shown that the proposed PET ranges and model appropriate for Indian mixed traffic conditions. To assess the suitability of model framework, model transferability was carried out with data collected at signalized intersection in Kolkata (India). Finally, this study can be helpful to rank the severity level of pedestrian safety in the crosswalk and improve the existing facilities at signalized intersections. © 2020 The Author(s).