Transport Networking Companies Demand and Flow Estimation in New York City

Given the burgeoning growth in transport networking companies (TNC)-based ride hailing systems and their growing adoption for trip making, it is important to develop modeling frameworks to understand TNC ride hailing demand flows at the system level. Two choice dimensions are identified: (1) a demand component that estimates origin level TNC demand at the taxi zone level and (2) a distribution component that analyzes how these trips from an origin are distributed across the region. The origin level demand is analyzed using linear mixed models while flows from origin to multiple destinations is analyzed using a multiple discrete-continuous extreme value (MDCEV) model. The data for the analysis is drawn from New York City Taxi and Limousine Commission for 12 months from January through December 2018. For this analysis, weekday morning peak hour demand and distribution patterns are examined. The model components are developed using a comprehensive set of independent variables. The model estimation results offer very intuitive results for origin demand and distribution of flows across destinations. The model was validated by predicting trips to destination taxi zones and it was found that predicted model performs well in identifying high preference destination zones. In addition, elasticity effects are computed by evaluating the percentage change in baseline marginal utility in response to increasing the value of exogenous variables by 10%, 25% and 50%, respectively.

Random-Parameter Multivariate Negative Binomial Regression for Modeling Impacts of Contributing Factors on the Crash Frequency by Crash Types

Highways provide the basis for safe and efficient driving. Road geometry plays a critical role in dynamic driving systems. Contributing factors such as plane, longitudinal alignment, and traffic volume, as well as drivers’ sight characteristics, determine the safe operating speed of cars and trucks. In turn, the operating speed influences the frequency and type of crashes on the highways. Methods. Independent negative binomial and Poisson models are considered as the base approaches to modeling in this study. However, random-parameter models reduce unobserved heterogeneity and obtain higher dimensions. Therefore, we propose the random-parameter multivariate negative binomial (RPMNB) model to analyze the influence of the traffic, speed, road geometry, and sight characteristics on the rear-end, bumping-guardrail, other, noncasualty, and casualty crashes. Subsequently, we compute the goodness-of-fit and predictive measures to confirm the superiority of the proposed model. Finally, we also calculate the elasticity effects to augment the comparison. Results. Among the significant variables, black spots, average annual daily traffic volume (AADT), operating speed of cars, speed difference of cars, and length of the present plane curve positively influence the crash risk, whereas the speed difference of trucks, length of the longitudinal slope corresponding to the minimum grade, and stopping sight distance negatively influence the crash risk. Based on the results, several practical and efficient measures can be taken to promote safety during the road design and operating processes. Moreover, the goodness-of-fit and predictive measures clearly highlight the greater performance of the RPMNB model compared to standard models. The elasticity effects across all the models show comparable performance with the RPMNB model. Thus, the RPMNB model reduces the unobserved heterogeneity and yields better performance in terms of precision, with more consistent explanatory power compared to the traditional models.

Theoretical and Experimental Study for An Improved Cycloid Drive Model

This paper describes an experimental and theoretical approach to evaluate cycloid drive reducer efficiency. The tests are carried out on 7.5 kW two-disc cycloid drive with a gear ratio of 19. The torque and speed are measured on the input and output shaft. The efficiency is calculated based on the obtained results. The main goal of the second part of the study is to deduce equations of cycloid reducer in order to predict and analyze experimental results. In this way, the following points are set for the simulation: a working condition in which the input speed and the output load are imposed; then, the output speed is determined by the gear ratio, and finally, the input torque is obtained by solving the dynamic problem. A new model for cycloidal reducers is proposed. This model is based on kinematics and dynamics of rigid bodies and a non-linear stiffness based on contact dynamics. The overall elasticity effects are all condensed between the input shaft and the cycloidal disk. The proposed model allows to predict the efficiency for several operational conditions and offer a drastic reduction of computational costs suitable for the optimization process.