Method for Estimating Vehicle-to-Vehicle Travel Time Variability Models at the Link and Network Levels of Freeways/Expressways through Censoring Mechanism

2019 ◽  
Vol 2673 (5) ◽  
pp. 548-563
Author(s):  
Whoibin Chung ◽  
Mohamed Abdel-Aty ◽  
Qing Cai ◽  
Raj Ponnaluri
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Travel Time ◽  
Linear Regression Model ◽  
Rainfall Amount ◽  
Time Variability ◽  
Speed Limits ◽  
Vehicle To Vehicle ◽  
Vehicle Travel Time ◽  
Travel Time Variability

A method was proposed to estimate vehicle-to-vehicle travel time variability (TTV) at the link and network levels of the entire freeway network. Standard deviation (SD) of travel time rate (TTR) was selected for the TTV. Models estimating the TTV were developed through a Tobit model using a left-censored limit. For the analysis of impact factors on TTV including day-to-day, the model included various types of variables: density, occupancy, traffic flow, link lengths, lane count, speed limits, rainfall amount, crash indicator, weekend indicator, and holiday indicator. According to the exploration and modeling results, TTR and its SD (vehicle-to-vehicle and day-to-day) have a statistical positive significant relationship at the link and network levels. Furthermore, it was confirmed that there is network fundamental diagram (NFD) at the network level. According to the modeling results, an increase in the number of lanes and speed limits, and crash occurrence, raises vehicle-to-vehicle and day-to-day TTV. However, TTV decreases if the link length is long. A high rainfall amount would reduce vehicle-to-vehicle TTV, but raise day-to-day TTV. Weekends and holidays increase vehicle-to-vehicle TTV but diminish day-to-day TTV. Finally, a linear regression model between TTV and TTR at the network level was developed. Through the relationship between the linear regression model and NFD, it is possible to develop new traffic management strategies and algorithms optimizing the vehicle-to-vehicle TTV at the network level. The developed vehicle-to-vehicle TTV models can be applied to validate the mobility improvement potential of vehicle-to-everything (V2X) communication applications on a segment, corridor, and regional scale.

Participation in Shared Mobility: An Analysis of the Influence of Walking and Public Transport Accessibility to Vehicles on Carsharing Membership in Montreal, Canada

2021 ◽  
pp. 036119812110322
Author(s):  
Mathilde Roblot ◽  
Geneviève Boisjoly ◽  
Ciari Francesco ◽  
Trépanier Martin
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Travel Time ◽  
Linear Regression Model ◽  
Public Transport ◽  
Key Factors ◽  
Sustainable Mobility ◽  
Transport Accessibility ◽  
Shared Mobility ◽  
Empirical Perspective

In the context of sustainable mobility policies, carsharing services have gained importance as an alternative to personal vehicles. In an effort to increase the adherence to and use of such services, several studies have explored the key factors that determine use and membership. Although the ease with which individuals can access shared vehicles appears to be a central determinant, few studies have specifically investigated how to measure station and vehicle accessibility. To fill this gap, this study seeks to systematically assess and compare the contribution of different accessibility indicators to modeling carsharing membership rate, using 2016 data from the Montreal carsharing company Communauto and from the Canadian census. Three indicators of accessibility to in-station vehicles are generated: walking only, public transport only, and multimodal accessibility (walking and public transport), considering a variety of travel time thresholds and cost functions. A linear regression model is then generated to assess the contribution of the different indicators to modeling membership rates, while controlling for socio-economic and commuting characteristics. The results show that walking accessibility, within 20 minutes, and public transport accessibility, within 40 minutes, are both key determinants of membership rate and in a complementary manner. The influence of public transport accessibility is positive and highest when walking accessibility is low. The results also demonstrate that the use of a cumulative or weighted-opportunity indicator is equally sound from an empirical perspective. The study is of relevance to researchers and planners wishing to better understand and model the influence of vehicle accessibility.

scholarly journals Analysis and Application of Log-Linear and Quantile Regression Models to Predict Bus Dwell Times

2019 ◽  
Vol 2673 (10) ◽  
pp. 118-128 ◽  
Author(s):  
Travis B. Glick ◽  
Miguel A. Figliozzi
Keyword(s):  
Linear Regression ◽  
Travel Time ◽  
Regression Models ◽  
Dwell Time ◽  
Time Variability ◽  
Linear Regression Models ◽  
Ongoing Research ◽  
Bus Stops ◽  
Log Linear ◽  
Travel Time Variability

Understanding the key factors that contribute to transit travel times and travel-time variability is an essential part of transit planning and research. Delay that occurs when buses service bus stops, dwell time, is one of the main sources of travel-time variability and has therefore been the subject of ongoing research to identify and quantify its determinants. Previous research has focused on testing new variables using linear regressions that may be added to models to improve predictions. An important assumption of linear regression models used in past research efforts is homoscedasticity or the equal distribution of the residuals across all values of the predicted dwell times. The homoscedasticity assumption is usually violated in linear regression models of dwell time and this can lead to inconsistent and inefficient estimations of the independent variable coefficients. Log-linear models can sometimes correct for the lack of homoscedasticity, that is, for heteroscedasticity in the residual distribution. Quantile regressions, which predict the conditional quantiles, rather than the conditional mean, are non-parametric and therefore more robust estimators in the presence of heteroscedasticity. This research furthers the understanding of established dwell determinants using these novel approaches to estimate dwell and provides a relatively simple approach to improve existing models at bus stops with low average dwell times.

Quantile Regression Analysis of Transit Travel Time Reliability with Automatic Vehicle Location and Farecard Data

2017 ◽  
Vol 2652 (1) ◽  
pp. 19-29 ◽  
Author(s):  
Zhenliang Ma ◽  
Sicong Zhu ◽  
Haris N. Koutsopoulos ◽  
Luis Ferreira
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Travel Time ◽  
Time Variability ◽  
Travel Time Reliability ◽  
Central Tendency ◽  
Automatic Vehicle Location ◽  
Conditional Mean ◽  
Vehicle Location ◽  
Travel Time Variability

Transit agencies increasingly deploy planning strategies to improve service reliability and real-time operational control to mitigate the effects of travel time variability. The design of such strategies can benefit from a better understanding of the underlying causes of travel time variability. Despite a significant body of research on the topic, findings remain influenced by the approach used to analyze the data. Most studies use linear regression to characterize the relationship between travel time reliability and covariates in the context of central tendency. However, in many planning applications, the actual distribution of travel time and how it is affected by various factors is of interest, not just the condition mean. This paper describes a quantile regression approach to analyzing the impacts of the underlying determinants on the distribution of travel times rather than its central tendency, using supply and demand data from automatic vehicle location and farecard systems collected in Brisbane, Australia. Case studies revealed that the quantile regression model provides more indicative information than does the conditional mean regression method. Moreover, most of the coefficients estimated from quantile regression are significantly different from the conditional mean–based regression model in terms of coefficient values, signs, and significance levels. The findings provide information related to the impacts of planning, operational, and environmental factors on speed and its variability. On the basis of this information, transit designers and planners can design targeted strategies to improve travel time reliability effectively and efficiently.

Physico-Chemical Analysis of Groundwater and Effect of Climatic Change in Angul-Talcher Area

2018 ◽  
Vol 6 (12) ◽  
pp. 26
Author(s):  
Aliva Bera ◽  
D.P. Satapathy
Keyword(s):  
Water Resources ◽  
Linear Regression ◽  
Regression Model ◽  
Climatic Change ◽  
Linear Regression Model ◽  
Ann Model ◽  
Quality Of Water ◽  
Physico Chemical ◽  
Simulation Based

In this paper, the linear regression model using ANN and the linear regression model using MS Excel were developed to estimate the physico-chemical concentrations in groundwater using pH, EC, TDS, TH, HCO3 as input parameters and Ca, Mg and K as output parameters. A comparison was made which indicated that ANN model had the better ability to estimate the physic-chemical concentrations in groundwater. An analytical survey along with simulation based tests for finding the climatic change and its effect on agriculture and water bodies in Angul-Talcher area is done. The various seasonal parameters such as pH, BOD, COD, TDS,TSS along with heavy elements like Pb, Cd, Zn, Cu, Fe, Mn concentration in water resources has been analyzed. For past 30 years rainfall data has been analyzed and water quality index values has been studied to find normal and abnormal quality of water resources and matlab based simulation has been done for performance analysis. All results has been analyzed and it is found that the condition is stable. 

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

Road Pricing under Travel Time Variability

2019 ◽  
Author(s):  
Gege Jiang ◽  
Hong Kam LO ◽  
Zheng LIANG
Keyword(s):  
Travel Time ◽  
Road Pricing ◽  
Time Variability ◽  
Travel Time Variability

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

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