Analytical Model for Optimal Departure Time Choice with Perception Errors for Planners

2019 ◽  
Vol 2673 (4) ◽  
pp. 963-974
Author(s):  
Junjie Zhang ◽  
Yunpeng Wang ◽  
Guangquan Lu ◽  
Miaomiao Liu
Keyword(s):  
Travel Time ◽  
Inverse Function ◽  
Normal Distributions ◽  
Departure Time ◽  
Choice Decision ◽  
Departure Time Choice ◽  
Mean And Variance ◽  
The Mean ◽  
Theoretical Results ◽  
Actual Travel Time

An extension of a recent framework for analyzing scheduling disutility with perception errors is derived. In such framework, the traveler has [Formula: see text] scheduling preferences. Although the actual travel time of travelers is influenced by departure time, travelers make a route-choice decision on the basis of their perceived travel time. Thus, we mainly investigate the effects of perception errors on the optimal departure time. The optimal departure time and expected disutility are obtained on the basis of the proposed scheduling disutility model with perception errors. The inverse function curve of a standardized normal distribution is introduced to geometrically analyze the positive correlation between perception errors and optimal departure time. Our empirical example further illustrates that departure time as an endogenous variable affects the expectation and variance of perceived travel time and implies that perception errors are related to departure time. The linear relationship between the mean and variance of perceived travel time and the departure time is analyzed through an empirical illustration. The expected optimal disutility and departure time are derived, provided that the mean and variance of perception errors and departure time satisfy linear dependence. Finally, the results of the analysis of scheduling services with standardized normal distributions agree with the theoretical results and linear assumption in this study.

scholarly journals Modeling Departure Time Choice of Car Commuters in Dhaka, Bangladesh

2021 ◽  
pp. 036119812110398
Author(s):  
Khatun Zannat ◽  
Charisma Farheen Choudhury ◽  
Stephane Hess
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Logit Model ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Data Sources ◽  
Similar Data ◽  
Schedule Delay ◽  
Departure Time ◽  
Departure Time Choice

Dhaka, one of the fastest-growing megacities in the world, faces severe traffic congestion leading to a loss of 3.2 million business hours per day. While peak-spreading policies hold the promise to reduce the traffic congestion levels, the absence of comprehensive data sources makes it extremely challenging to develop econometric models of departure time choices for Dhaka. This motivates this paper, which develops advanced discrete choice models of departure time choice of car commuters using secondary data sources and quantifies how level-of-service attributes (e.g., travel time), socio-demographic characteristics (e.g., type of job, income, etc.), and situational constraints (e.g., schedule delay) affect their choices. The trip diary data of commuters making home-to-work and work-to-home trips by personal car/ride-hailing services (957 and 934 respectively) have been used in this regard. Given the discrepancy between the stated travel times and those extracted using the Google Directions API, a sub-model is developed first to derive more reliable estimates of travel time throughout the day. A mixed multinomial logit model and a simple multinomial logit model are developed for outbound and return trip, respectively, to capture the heterogeneity associated with different departure time choice of car commuters. Estimation results indicate that the choices are significantly affected by travel time, schedule delay, and socio-demographic factors. The influence of type of job on preferred departure time (PDT) has been estimated using two different distributions of PDT for office employees and self-employed people (Johnson’s SB distribution and truncated normal respectively). The proposed framework could be useful in other developing countries with similar data issues.

Departure and travel time model for the temporal distribution of morning rush-hour traffic congestion

2019 ◽  
Vol 31 (02) ◽  
pp. 2050023
Author(s):  
Sida Luo
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Temporal Distribution ◽  
Time Estimation ◽  
Wave Model ◽  
Model Parameters ◽  
Travel Time Estimation ◽  
Time Model ◽  
Departure Time ◽  
Departure Time Choice

The chronic traffic congestion undermines the level of satisfaction within a society. This study proposes a departure time model for estimating the temporal distribution of morning rush-hour traffic congestion over urban road networks. The departure time model is developed based on the point queue model that is used for estimating travel time. First, we prove the effectiveness of the travel time model (i.e. point queue), showing that it gives the same travel time estimation as the kinematic wave model does for a road with successive bottlenecks. Then, a variant of the bottleneck model is developed accordingly, aiming to capture travelers’ departure time choice for commute trips. The proposed departure time model relaxes a traditional assumption that the last commuter experiences the free flow travel time and considers travelers’ unwillingness of late arrivals for work. Numerical experiments show that the morning rush-hour generally starts at 7:29 am and ends at 8:46 am with a traffic congestion delay index (TCDI) of 2.164 for Beijing, China. Furthermore, the estimation of rush-hour start and end time is insensitive to most model parameters including the proportion of travelers who tend to arrive at work earlier than their schedules.

Stochastic Modeling of Diffusivity and Constant-Rate Pumping Tests in Heterogeneous Aquifers in a Tomographic Setup and Its Application to Field Measurements

2021 ◽  
Author(s):  
Kan Bun Cheng ◽  
Gedeon Dagan ◽  
Avinoam Rabinovich
Keyword(s):  
Hydraulic Conductivity ◽  
Travel Time ◽  
Field Measurements ◽  
Specific Storage ◽  
First Order ◽  
Hydraulic Tomography ◽  
Constant Rate ◽  
Mean And Variance ◽  
The Mean ◽  
Aquifer Properties

<p>Characterization of spatially variable aquifer properties is a necessary first step towards modeling flow and transport. An emerging technique in hydraulic tomography, known as diffusivity tests, consist of injecting (or pumping) a volume of water through short segments of a well for a short time and measuring the travel time of the peak of the head signal at different points in the surrounding aquifer volume. In our stochastic model, the specific storage is assumed to be constant, while the hydraulic conductivity of the heterogeneous aquifer is modeled as a random lognormal field. The axi-symmetric anisotropic structure is characterized by a few parameters (logconductivity mean and variance and horizontal and vertical integral scales). The mean and variance of the peak travel time are then determined as a function of distance from an instantaneous source by solving the flow equation using a first-order approximation in the logconductivity variance. The mean travel time is recast in terms of the equivalent conductivity, which decreases from the harmonic mean near the source to the effective conductivity in uniform flow for a sufficiently large distance. Similarly, the variance drops from its maximum near the source to a small value.</p><p>A different type of tomographic test is the constant-rate pumping one. We propose to apply the first order stochastic approach to the data from the Boise Hydrogeophysical Research site (BHRS) to characterize the aquifer properties by estimating heterogeneity statistical parameters. Equivalent properties are first calculated by matching a homogeneous aquifer solution to the pointwise data to obtain a spatially varying hydraulic conductivity (K<sub>eq</sub>) and storativity (S<sub>s,eq</sub>). Then the statistical properties of K and S<sub>s</sub> are to be computed by a best fit between the theoretically derived statistical moments of the equivalent random properties (K<sub>eq</sub>, S<sub>s,eq</sub>) and those from field measurements. Our preliminary results indicate that the proposed stochastic methodology is robust and reliable as well as computationally more efficient than the conventional hydraulic tomography techniques.</p>

scholarly journals Weighted statistical binary patterns for facial feature representation

2021 ◽  
Author(s):  
Hung Phuoc Truong ◽  
Thanh Phuong Nguyen ◽  
Yong-Guk Kim
Keyword(s):  
Comprehensive Evaluation ◽  
Facial Feature ◽  
Input Image ◽  
Feature Representation ◽  
Illumination Variation ◽  
Straight Line ◽  
Mean And Variance ◽  
The Mean ◽  
Face Datasets ◽  
Degraded Images

AbstractWe present a novel framework for efficient and robust facial feature representation based upon Local Binary Pattern (LBP), called Weighted Statistical Binary Pattern, wherein the descriptors utilize the straight-line topology along with different directions. The input image is initially divided into mean and variance moments. A new variance moment, which contains distinctive facial features, is prepared by extracting root k-th. Then, when Sign and Magnitude components along four different directions using the mean moment are constructed, a weighting approach according to the new variance is applied to each component. Finally, the weighted histograms of Sign and Magnitude components are concatenated to build a novel histogram of Complementary LBP along with different directions. A comprehensive evaluation using six public face datasets suggests that the present framework outperforms the state-of-the-art methods and achieves 98.51% for ORL, 98.72% for YALE, 98.83% for Caltech, 99.52% for AR, 94.78% for FERET, and 99.07% for KDEF in terms of accuracy, respectively. The influence of color spaces and the issue of degraded images are also analyzed with our descriptors. Such a result with theoretical underpinning confirms that our descriptors are robust against noise, illumination variation, diverse facial expressions, and head poses.

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