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Journal articles on the topic 'Time-of-departure'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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1

KOBAYASHI, Kiyoshi, Makoto OKUMUA, and Mitsuzo NAGANO. "DEPARTURE TIME DISTRIBUTION OF RAILWAY COMMUTING." INFRASTRUCTURE PLANNING REVIEW 14 (1997): 895–906. http://dx.doi.org/10.2208/journalip.14.895.

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2

Shumsky, Robert A. "Real-Time Forecasts of Aircraft Departure Queues." Air Traffic Control Quarterly 5, no. 4 (October 1997): 281–308. http://dx.doi.org/10.2514/atcq.5.4.281.

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3

Yamamoto, Toshiyuki, Satoshi Fujii, Ryuichi Kitamura, and Hiroshi Yoshida. "Analysis of Time Allocation, Departure Time, and Route Choice Behavior Under Congestion Pricing." Transportation Research Record: Journal of the Transportation Research Board 1725, no. 1 (January 2000): 95–101. http://dx.doi.org/10.3141/1725-13.

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Driver behavior under congestion pricing is analyzed to evaluate the effects of alternative congestion pricing schemes. The analysis, which is based on stated preference survey results, focuses on time allocation, departure time choice, and route choice when a congestion pricing scheme is implemented on toll roads in Japan. A unique feature of the model system of this study is that departure time choice and route choice are analyzed in conjunction with the activities before and after the trip. A time allocation model is developed to describe departure time choice, and a route and departure time choice model is developed as a multinomial logit model with alternatives representing the choice between freeways and surface streets and, for departure time, the choice from among before, during, or after the period when congestion pricing is in effect. The results of the empirical analysis suggest that departing during the congestion pricing period tends to have higher utilities and that a worker and a nonworker have quite different utility functions. The comparative analysis of different congestion pricing schemes is carried out based on the estimated parameters. The results suggest that the probability of choosing each alternative is stable even if the length of the congestion pricing period changes, but a higher congestion price causes more drivers to change the departure time to before the congestion pricing period.

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4

TAKADA, Kazuyuki, Takanori SUZUKI, and Makoto FUJIU. "DEPARTURE TIME DECISION CONSIDERING DELAY OF RAILWAY OPERATION." Journal of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management) 68, no. 5 (2012): I_1071—I_1077. http://dx.doi.org/10.2208/jscejipm.68.i_1071.

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5

KIMURA, Yusuke, Makoto OKUMURA, and Maiko SAKAMOTO. "Reverse Analysis of Preferred Departure Time of Air Passengers." INFRASTRUCTURE PLANNING REVIEW 25 (2008): 633–39. http://dx.doi.org/10.2208/journalip.25.633.

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6

Cong, Jing Shuai, and Yu Zhao Zhang. "Research on Optimization of the Passenger Train Departure Time Domain for Reducing Passenger Train Stock Amount." Applied Mechanics and Materials 641-642 (September 2014): 700–703. http://dx.doi.org/10.4028/www.scientific.net/amm.641-642.700.

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A method was proposed to reduce the amount of train stock by optimizing the passenger train departure time domain. The objective function is the minimum total turn-round time of passenger train stock. The constraints include the dwell time in departure station and turn-back station, the interval time. The optimization model of the passenger train departure time domain was built. Design the heuristic algorithm to solve the proposed model. Taking the Zhengzhou station as an example, each passenger train departure time domain is calculated by utilizing the proposed model. Comparing the calculated departure time domain with the real departure time, the effectiveness of the model is calculated.

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7

IRYO, Takamasa, Toshio YOSHII, and Yasuo ASAKURA. "MATHEMATICAL ANALYSIS OF EQUILIBRIUM IN DEPARTURE TIME CHOICE PROBLEMS." Doboku Gakkai Ronbunshu, no. 779 (2005): 105–18. http://dx.doi.org/10.2208/jscej.2005.779_105.

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8

Zhang, Zhao-Ze, Hai-Jun Huang, and Tie-Qiao Tang. "Impacts of preceding information on travelers’ departure time behavior." Physica A: Statistical Mechanics and its Applications 505 (September 2018): 523–29. http://dx.doi.org/10.1016/j.physa.2018.03.067.

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9

Heydecker, B. G., and J. D. Addison. "Analysis of Dynamic Traffic Equilibrium with Departure Time Choice." Transportation Science 39, no. 1 (February 2005): 39–57. http://dx.doi.org/10.1287/trsc.1030.0075.

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10

Bajwa, Shamas, Shlomo Bekhor, Masao Kuwahara, and Edward Chung. "DISCRETE CHOICE MODELING OF COMBINED MODE AND DEPARTURE TIME." Transportmetrica 4, no. 2 (January 2008): 155–77. http://dx.doi.org/10.1080/18128600808685681.

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11

Mahmassani, Hani S., and Gang-Len Chang. "Experiments with departure time choice dynamics of urban commuters." Transportation Research Part B: Methodological 20, no. 4 (August 1986): 297–320. http://dx.doi.org/10.1016/0191-2615(86)90045-7.

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12

Bhat, Chandra R., and Jennifer L. Steed. "A continuous-time model of departure time choice for urban shopping trips." Transportation Research Part B: Methodological 36, no. 3 (March 2002): 207–24. http://dx.doi.org/10.1016/s0191-2615(00)00047-3.

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13

Tamminga, Guus, Huizhao Tu, Winnie Daamen, and Serge Hoogendoorn. "Influence of Departure Time Spans and Corresponding Network Performance on Evacuation Time." Transportation Research Record: Journal of the Transportation Research Board 2234, no. 1 (January 2011): 89–96. http://dx.doi.org/10.3141/2234-10.

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14

Zhang, Junjie, Yunpeng Wang, Guangquan Lu, and Miaomiao Liu. "Analytical Model for Optimal Departure Time Choice with Perception Errors for Planners." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 4 (April 2019): 963–74. http://dx.doi.org/10.1177/0361198119838839.

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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.

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15

De Palma, André, Asad J. Khattak, and Deepak Gupta. "Commuters’ Departure Time Decisions in Brussels, Belgium." Transportation Research Record: Journal of the Transportation Research Board 1607, no. 1 (January 1997): 139–46. http://dx.doi.org/10.3141/1607-19.

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Factors that influence commuters’ departure time decisions are explored, especially the trade-off between travel time and schedule delay. Stated and reported behavior data obtained from a survey of commuters in Brussels, Belgium, were used to analyze the influence of socioeconomic and contextual variables. The key findings were as follows. Daily schedules for flextime and fixed-time commuters were quite similar, suggesting that flextime commuters do not extensively use their flexibility to avoid peak-period congestion. When commuters changed their departure times between home and work, their arrival times shifted by a similar amount. This implies that the shortening of travel time is not as critical as other reasons, such as requirements and personal convenience, in motivating departure time changes. Furthermore, 35 to 50 percent of the respondents were unwilling to change their departure times to save 10 min of travel time. Therefore, departure time changes may not be feasible in many cases for the range of travel times encountered in urban areas. Among those willing to make further trade-offs by changing departure times, the values for the early and late schedule delay–travel time trade-off were similar for both the stated and the reported preferences and were broadly consistent with those from other studies. The travel time–schedule delay trade-off values are calculated for the a.m. and p.m. commutes. Commuters who experienced longer travel times were more likely to change their departure times. When changing departure times, females and managers were less likely to depart from home later than usual, and managers were also more likely to depart earlier than usual. To analyze relationships empirically, ordinary-least-squares and tobit models of departure time are estimated. Finally, the implications are discussed.

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16

Zhu, Xinhua, Nan Li, Yu Sun, Hongfei Zhang, Kai Wang, and Sang-Bing Tsai. "A Study on the Strategy for Departure Aircraft Pushback Control from the Perspective of Reducing Carbon Emissions." Energies 11, no. 9 (September 17, 2018): 2473. http://dx.doi.org/10.3390/en11092473.

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In order to reduce the taxiing time of departing aircraft and reduce the fuel consumption and exhaust emissions of the aircraft, Shanghai Hongqiao Airport was taken as an example to study the control strategy for aircraft departure. In this paper, the influence of the number of departure aircraft on the runway utilization rate, the takeoff rate, and the departure rate of flight departures under the conditions of airport runway capacity constraints are studied. The influence of factors, such as the number of departure aircraft, the gate position of the aircraft, and the configuration of airport arrival and departure runways, on the aircraft taxiing time for departure is analyzed. Based on a multivariate linear regression equation, a time prediction model of aircraft departure taxiing time is established. The fuel consumption and pollutant emissions of aircraft are calculated. The experimental results show that, without reducing the utilization rate of the runway and the departure rate of flights, implementing a reasonable pushback number for control of departing aircraft during busy hours can reduce the departure taxiing time of aircraft by nearly 32%, effectively reducing the fuel consumption and pollutant emissions during taxiing on the airport surface.

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17

Ozbay, Kaan, and Ozlem Yanmaz-Tuzel. "Valuation of travel time and departure time choice in the presence of time-of-day pricing." Transportation Research Part A: Policy and Practice 42, no. 4 (May 2008): 577–90. http://dx.doi.org/10.1016/j.tra.2007.12.002.

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18

Xue, Yunqiang, Huishu Fan, and Hongzhi Guan. "Commuter Departure Time Choice Considering Parking Space Shortage and Commuter’s Bounded Rationality." Journal of Advanced Transportation 2019 (April 7, 2019): 1–7. http://dx.doi.org/10.1155/2019/4864287.

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In order to better understand how commuters decide departure time considering parking space shortage and commuters’ bounded rationality, the reference point hypothesis of prospect theory is applied in the departure time decision-making. Commuter personal perception differences, the road congestion situation, destination parking status, and other factors were also analysed in the influence of commuter departure time choice. Based on prospect theory, an experiment was designed to investigate the intention of the commuter departure time choice. The experiment results show that the commuter’s travel satisfaction and the departure time choice of the next trip are related to the parking space residual status after the commuter arrives at the destination. The satisfaction degree of the commuter is reduced, with the decrease of the remaining parking spaces. If the commuter is satisfied with the travel result, the commuter’s departure time of next trip tends to be later. In the case of illegal parking, different penalty measures may lead to different decisions of next departure time choice. A commuter tends to depart earlier when more severe punishment for illegal parking is enforced. The research results can reveal to some degree the travellers’ departure time choice behaviour when they face the risk of no parking spaces and provide a theoretical and practical support for parking management and car travelling decision.

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19

Xiong, Chenfeng, and Lei Zhang. "Positive Model of Departure Time Choice under Road Pricing and Uncertainty." Transportation Research Record: Journal of the Transportation Research Board 2345, no. 1 (January 2013): 117–25. http://dx.doi.org/10.3141/2345-15.

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A novel positive model was developed for departure time choice under road pricing and uncertainty at individual levels, and the consequent system-level dynamic properties were also analyzed. The proposed modeling framework avoided assumptions of substantial rationality and focused on how individuals actually make decisions. Bayesian learning, knowledge updating, search, and decision making under uncertainty were modeled in the framework. Then time-dependent departure patterns along with other system performance were investigated in a series of agent-based simulation experiments. The way in which individuals actually chose departure time under various supply- and demand-side uncertainty scenarios was explored for the effect of the scenarios on system performance and its dynamic properties.

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20

Cheng, Yan, Xiafei Ye, and Taku Fujiyama. "Identifying Crowding Impact on Departure Time Choice of Commuters in Urban Rail Transit." Journal of Advanced Transportation 2020 (June 23, 2020): 1–16. http://dx.doi.org/10.1155/2020/8850565.

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Crowding in urban rail transit is an inevitable issue for most of the high-density cities across the world, especially during peak time. For commuters who have considerably fixed destination arrival times, departure time choice is an important tool to adjust their trips. The ignorance of crowding impact on commuters’ departure time choice in urban rail transit may cause errors in forecasting dynamic passenger flow during peak time in urban rail transit. The paper develops a mixed logit model to identify how crowding impacts the departure time choice of commuters and their taste variation. Arrival time value was firstly measured in a submodel by applying the reference point approach and then integrated to the main model. Considering the characteristics of human perception, we divided crowding into five grades with distinct circumstances. All parameter distributions were assumed based on their empirical distributions revealed through resampling. The data from Shanghai Metro used for estimation were collected by a specifically designed survey, which combines revealed preference questions and stated preference experiments to investigate the willingness and extent of changing departure time choice of passengers who experienced various grades and duration of crowding in the most crowded part. The result shows that an asymmetric valuation model with preferred arrival time as the only reference point best captured commuters’ responses to arrival time. The departure time choice model clearly identified that only crowding ranging from Grades 3 to 5 had an impact on commuters’ departure time choice. The parameters of crowding costs can be assumed to follow transformed lognormal distributions. It is found that the higher the grade of crowding is, the bigger the impact each unit of crowding cost has on commuters’ departure time choice, while commuters’ tastes get more concentrated when crowded situation upgrades. The model in this paper can help policymakers better understand the interaction between commuters’ departure time choice and crowding alleviation.

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21

Srinivasan, Karthik K., and Zhiyong Guo. "Day-to-Day Evolution of Network Flows Under Departure Time Dynamics in Commuter Decisions." Transportation Research Record: Journal of the Transportation Research Board 1831, no. 1 (January 2003): 47–56. http://dx.doi.org/10.3141/1831-06.

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Day-to-day dynamics in an urban traffic network induced by departure time dynamics in commuter decisions are investigated. This investigation relaxes some key restrictions about fixed departure time and equilibrium assumptions to analyze the stability and performance of urban traffic networks over a multiple day planning horizon. A simulation-based framework is developed to analyze day-to-day dynamics by integrating an empirically calibrated model of dynamic departure time decisions with a dynamic network assignment model. Computational experiments are used to investigate the effect of the following experimental factors: recurrent network congestion level, time-dependent loading profile, and users’ sensitivity to commute experience and trip-time volatility on network performance and reliability. The findings provide evidence of considerable day-to-day variations and stochasticity in network flows and performance, even under the assumption of fixed routes and in the absence of information. The results indicate that ( a) the network performance under departure time dynamics can deviate significantly from equilibrium; ( b) the departure time adjustment process is remarkably stable and reaches stationarity, although the departure time choices do not appear to be at equilibrium; ( c) departure time dynamics introduce significant volatility in trip times from day to day; and ( d) increasing the sensitivity of users to commute and network performance attributes (schedule delay, trip-time variability) can lead to more stable system behavior and reliability. These results have important implications for estimation of time-dependent origin–destination matrices, dynamic network analysis, and effective congestion management strategies.

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22

Eikenaar, Cas, Florian Müller, Clara Leutgeb, Sven Hessler, Konstantin Lebus, Philip D. Taylor, and Heiko Schmaljohann. "Corticosterone and timing of migratory departure in a songbird." Proceedings of the Royal Society B: Biological Sciences 284, no. 1846 (January 11, 2017): 20162300. http://dx.doi.org/10.1098/rspb.2016.2300.

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Bird migration entails replenishing fuel stores at stopover sites. There, individuals make daily decisions whether to resume migration, and must also decide their time of departure. Variation in departure timing affects the total time required to complete a migratory journey, which in turn affects fitness through arrival time at the breeding and wintering grounds. It is well established that stopover departure decisions are based on cues from innate rhythms, intrinsic factors and extrinsic factors. Yet, virtually nothing is known about the physiological mechanism(s) linking these cues to departure decisions. Here, we show for a nocturnal migratory songbird, the northern wheatear ( Oenanthe oenanthe ), that baseline corticosterone levels of birds at stopover increased both over the migratory season and with wind assistance towards the migratory destination. Corticosterone in turn predicted departure probability; individuals with high baseline corticosterone levels were more likely to resume migration on a given night. Corticosterone further predicted the departure time within the night, with high baseline levels being associated with early departures. These novel findings indicate that corticosterone may be mediating between departure cues and the timing of departure from a stopover site, which is a major step towards understanding the hormonal control of animal migration.

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23

Kuwahara, Masao. "Review of Theory on Departure Time Choice for Highway Traffic." Doboku Gakkai Ronbunshu, no. 604 (1998): 73–84. http://dx.doi.org/10.2208/jscej.1998.604_73.

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24

Wu, Jun, Shen Qi Ding, Kui Yu, and Xiao Biao Li. "Real-Time Detection Method on Draft of Inland Departure Ships." Advanced Materials Research 562-564 (August 2012): 1972–76. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.1972.

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For ensuring the safety of inland river shipping and avoiding super draft, it is of significance to propose a method to detect draft of inland departure ship. The real-time detection method on draft of inland departure ship is proposed, the measurement principle of ship draft is deeply analyzed, and the small scale experiment has been carried out. Experimental results show that as ship speed inicreases, the ship draft is of gradually declining tendency, and the measurement error is of gradually increasing tendency, the minium error is -4.2%, the maximum error is -11.4%. The results prove the possibility of this method , and also indicate that ship speed has large influence on measured ship draft.

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25

UCHIDA, Takashi, Yasunori IIDA, and Akira MATSUSHITA. "Empirical Analysis on the Departure Time Choice of Commuting Drivers." INFRASTRUCTURE PLANNING REVIEW 10 (1992): 39–46. http://dx.doi.org/10.2208/journalip.10.39.

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26

Iryo, Takamasa. "An analysis of instability in a departure time choice problem." Journal of Advanced Transportation 42, no. 3 (July 2008): 333–58. http://dx.doi.org/10.1002/atr.5670420308.

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27

S, Gajanand M., and S. Sivanandham. "Comparison of platoon formations using a departure time coordination heuristic." International Journal of Operational Research 1, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijor.2020.10033186.

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28

Mannering, Fred, Soon-Gwan Kim, Woodrow Barfield, and Linda Ng. "Statistical analysis of commuters' route, mode, and departure time flexibility." Transportation Research Part C: Emerging Technologies 2, no. 1 (March 1994): 35–47. http://dx.doi.org/10.1016/0968-090x(94)90018-3.

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29

Xie, Pei Xi, and Wei Yang. "Safe Departure Interval of Bullet Trains: Based on Block Section." Applied Mechanics and Materials 253-255 (December 2012): 1177–80. http://dx.doi.org/10.4028/www.scientific.net/amm.253-255.1177.

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One of the important ways to improve the transport capacity of China is to reduce time of departure interval, which must be based on the premise that safety is guaranteed. In this paper, based on block section, a safe departure interval (SDI) model is constructed, which focus on technology and management factors in the train operation. Using SDI model, safe departure interval time of Shanghai-Hangzhou railway line is calculated, and sensitivity of some factors are analyzed. The rustles indicate that there is plenty of room for decrease time of departure interval safely.

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30

Xu, Ziyi, Quchao Wang, Duo Li, Menghan Hu, Nan Yao, and Guangtao Zhai. "Estimating Departure Time Using Thermal Camera and Heat Traces Tracking Technique." Sensors 20, no. 3 (January 31, 2020): 782. http://dx.doi.org/10.3390/s20030782.

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Advancement in science and technology is playing an increasingly important role in solving difficult cases at present. Thermal cameras can help the police crack difficult cases by capturing the heat trace on the ground left by perpetrators, which cannot be spotted by the naked eye. Therefore, the purpose of this study is to establish a thermalfoot model using thermal imaging system to estimate the departure time. To this end, in the current work, we use a thermal camera to acquire the thermal sequence left on the floor, and convert it into the heat signal via image processing algorithm. We establish the model of thermalfoot print as we observe that the residual temperature would exponentially decrease with the departure time according to Newton’s Law of Cooling. The correlation coefficients of 107 thermalfoot models derived from the corresponding 107 heat signals are basically above 0.99. In a validation experiment, a residual analysis is conducted and the residuals between estimated departure time points and ground-truth times are almost within a certain range from −150 s to +150 s. The reverse accuracy of the thermalfoot model for estimating departure time at one-third, one-half, two-thirds, three-fourths, four-fifths, and five-sixths capture time points are 71.96%, 50.47%, 42.06%, 31.78%, 21.70%, and 11.21%, respectively. The results of comparison experiments with two subjective evaluation methods (subjective 1: we directly estimate the departure time according to obtained local curves; subjective 2: we utilize auxiliary means such as a ruler to estimate the departure time based on obtained local curves) further demonstrate the effectiveness of thermalfoot model for detecting the departure time inversely. Experimental results also demonstrated that the thermalfoot model has good performance on the departure time reversal within a short time window someone leaves, whereas it is probably only approximately 15% to accurately determine the departure time via thermalfoot model within a long time window someone leaves. The influence of outliers, ROI (Region of Interest) selection, ROI size, different capture time points and environment temperature on the performance of thermalfoot model on departure time reversal can be explored in the future work. Overall, the thermalfoot model can help the police solve crimes to some extent, which in turn brings more guarantees for people’s health, social security, and stability.

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31

Gong, Huibo, Xumei Chen, Lei Yu, and Lijuan Wu. "An application-oriented model of passenger waiting time based on bus departure time intervals." Transportation Planning and Technology 39, no. 4 (April 4, 2016): 424–37. http://dx.doi.org/10.1080/03081060.2016.1160583.

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32

Afandizadeh Zargari, Shahriar, and Farshid Safari. "Using Clustering Methods in Multinomial Logit Model for Departure Time Choice." Journal of Advanced Transportation 2020 (January 15, 2020): 1–12. http://dx.doi.org/10.1155/2020/7382569.

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Travellers have to make some decisions for each trip, and one of them is the choice of departure time. Discrete choice models have been employed as an approach to departure time modelling by many researchers. In this method, preparing choice set is a primary challenge which involves the definition of some departure periods to be selected by the traveller. In this research, choice sets were formed by applying the clustering methods on departure times. Afterwards, we developed Multinomial Logit (MNL) models on different choice sets and compared the models. The data used throughout this research belonged to Mashhad City. Research results indicated that Ward’s hierarchical clustering method is improper for time discretization; furthermore, the K-means clustering method is more efficient than the expectation maximization and K-medoids methods in the time discretization for MNL modelling. The developed model (based on K-means clustering method) accurately predicts departure time for 58% of persons within the test group, which reflects the effectiveness of the resulting model compared to the 36% which is obtained without the model.

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33

Steed, Jennifer L., and Chandra R. Bhat. "On Modeling Departure-Time Choice for Home-Based Social/Recreational and Shopping Trips." Transportation Research Record: Journal of the Transportation Research Board 1706, no. 1 (January 2000): 152–59. http://dx.doi.org/10.3141/1706-18.

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The existing literature on departure-time choice has primarily focused on work trips. Departure-time choice for nonwork trips, which constitute an increasingly large proportion of urban trips, is examined. Discrete choice models are estimated for home-based social/recreational and home-based shopping trips using the 1996 activity survey data collected in the Dallas—Fort Worth metropolitan area. The effects of individual and household sociodemographics, employment attributes, and trip characteristics on departure-time choice are presented and discussed. The results indicate that departure-time choice for social/recreational trips and shopping trips is determined for the most part by individual or household sociodemographics and employment characteristics, and to a lesser extent by trip level-of-service characteristics. This suggests that departure times for social/recreational and shopping trips are not as flexible as one might expect and are confined to certain times of day because of overall scheduling constraints. Future methodological and empirical extensions of the current research are identified.

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34

Brown, Timothy L., John Gaspar, Dawn Marshall, and John D. Lee. "How Does Lane Departure Warning Effectiveness Vary by Severity of Departure." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 1929–33. http://dx.doi.org/10.1177/1541931213601968.

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Lane departures are a significant traffic safety problem. They can be attributed to a variety of types of impairment including the increasing prevalence of distracted driving. Research to date has focused on how drivers respond to imminent lane departure warnings, often with short time to line crossing at the onset of the alert. This paper examines how this effectiveness changes over a range of severities associated with various times to line crossing. Although warning systems are generally beneficial in reducing the severity of lane departures, time to line crossing had mixed effects on lane departures. Longer time to line crossing was associated with a greater likelihood of departing the lane but with less severe lane departures. Additionally, an active warning that provides steering torque to help correct lane departures showed decreased effectiveness with increasing time to line crossing in terms of likelihood of lane departure and duration and area of lane departure. These results point to the importance of considering the range of situations in which a warning may be issued, not just the most severe cases.

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35

IRYO, Takamasa, and Yasuo ASAKURA. "ANALYSIS ON MARGINAL COST OF TRAFFIC CONGESTION CONSIDERING DEPARTURE TIME CHOICE." Doboku Gakkai Ronbunshuu D 62, no. 1 (2006): 96–112. http://dx.doi.org/10.2208/jscejd.62.96.

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36

Ettema, Dick, and Harry Timmermans. "Modeling Departure Time Choice in the Context of Activity Scheduling Behavior." Transportation Research Record: Journal of the Transportation Research Board 1831, no. 1 (January 2003): 39–46. http://dx.doi.org/10.3141/1831-05.

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Development of a model of departure choice behavior in the context of activity-travel scheduling behavior is reported. Based on a brief characterization of the literature, some key properties of the desired model are identified. Then, the theoretical framework is outlined and an operational model is derived. Next, the model is estimated with activity-travel diary data, collected in the context of the Albatross model system. The results are promising. Avenues of future research are discussed.

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37

Spencer, Thomas L., and Antonio A. Trani. "Predictive Models of Departure and Arrival Occupancy Time and Takeoff Distance." Journal of Air Transportation 27, no. 2 (April 2019): 81–95. http://dx.doi.org/10.2514/1.d0133.

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38

Patterson, Judith, George J. Noel, David A. Senzig, Christopher J. Roof, and Gregg G. Fleming. "Analysis of Departure and Arrival Profiles Using Real-Time Aircraft Data." Journal of Aircraft 46, no. 4 (July 2009): 1094–103. http://dx.doi.org/10.2514/1.42432.

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Sokolov, Vadim, Jeffrey Larson, Todd Munson, Josh Auld, and Dominik Karbowski. "Maximization of Platoon Formation Through Centralized Routing and Departure Time Coordination." Transportation Research Record: Journal of the Transportation Research Board 2667, no. 1 (January 2017): 10–16. http://dx.doi.org/10.3141/2667-02.

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Platooning allows vehicles to travel with a small intervehicle distance in a coordinated fashion because of vehicle-to-vehicle connectivity. When applied at a larger scale, platooning creates significant opportunities for energy savings because of reduced aerodynamic drag, as well as increased road capacity and a reduction in congestion resulting from shorter vehicle headways. These potential savings are maximized, however, if platooning-capable vehicles spend most of their travel time within platoons. Ad hoc platoon formation may not ensure a high rate of platoon driving. This paper considers the problem of central coordination of platooning-capable vehicles. Coordination of their routes and departure times can maximize the fuel savings afforded by platooning vehicles. The resulting problem is a combinatorial optimization problem that considers the platoon coordination and vehicle routing problems simultaneously. The methodology is demonstrated through evaluation of the benefits of a coordinated solution and comparison with the uncoordinated case when platoons form only in an ad hoc manner. The coordinated and uncoordinated scenarios are compared on a grid network with various assumptions about demand and the time vehicles are willing to wait.

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Shabanpour, Ramin, Nima Golshani, Sybil Derrible, Abolfazl (Kouros) Mohammadian, and Mohammad Miralinaghi. "Joint Discrete-Continuous Model of Travel Mode and Departure Time Choices." Transportation Research Record: Journal of the Transportation Research Board 2669, no. 1 (January 2017): 41–51. http://dx.doi.org/10.3141/2669-05.

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This paper presents a cluster-based joint modeling approach to investigating heterogeneous travelers’ behavior toward trip mode and departure time choices by considering those choices as a joint decision. First, a two-step clustering algorithm was applied to classify travelers into six distinct clusters to account for the heterogeneity in their decision-making behavior. Then, a joint discrete-continuous model was proposed for each cluster, in which the travel mode and departure time were estimated by a multinomial logit and a log-linear regression model, respectively. These two models were jointly estimated with a copula approach. For an investigation of the performance of the proposed approach, its results were compared with an aggregate joint model on all nonclustered observations to assess the potential benefits of population clustering. The goodness-of-fit measures and prediction accuracy results demonstrated that the proposed cluster-based joint model significantly outperformed the aggregate joint model. Further, the variations in the estimated parameters of different clusters indicated significant behavioral differences across clusters. Hence, the proposed cluster-based joint model, while offering higher accuracy, possesses a significant potential for transportation policy making because it has the capability to target different types of travelers on the basis of their decision-making behavior.

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FURUE, Katsunari, and Masashi KUWANO. "An Analysis on Acceptability of Departure Time Change in Rural Area." JOURNAL OF RURAL PLANNING ASSOCIATION 38, Special_Issue (November 20, 2019): 156–62. http://dx.doi.org/10.2750/arp.38.156.

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Thorhauge, Mikkel, Sonja Haustein, and Elisabetta Cherchi. "Accounting for the Theory of Planned Behaviour in departure time choice." Transportation Research Part F: Traffic Psychology and Behaviour 38 (April 2016): 94–105. http://dx.doi.org/10.1016/j.trf.2016.01.009.

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Iryo, Takamasa. "Instability of departure time choice problem: A case with replicator dynamics." Transportation Research Part B: Methodological 126 (August 2019): 353–64. http://dx.doi.org/10.1016/j.trb.2018.08.005.

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Sumi, Tomonori, Yoshiji Matsumoto, and Yasuyuki Miyaki. "Departure time and route choice of commuters on mass transit systems." Transportation Research Part B: Methodological 24, no. 4 (August 1990): 247–62. http://dx.doi.org/10.1016/0191-2615(90)90001-f.

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Vautard, Félix, Chengxi Liu, and Oskar Fröidh. "Improving interregional rail timetabling through welfare assessment of departure time shifts." Journal of Rail Transport Planning & Management 17 (March 2021): 100233. http://dx.doi.org/10.1016/j.jrtpm.2020.100233.

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Hohzaki, Ryusuke, Susumu Fujii, and Hiroakl Sandoh. "A solution for the shortest time path problem with time-windows of arrival and departure time constraints." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 73, no. 12 (1990): 97–105. http://dx.doi.org/10.1002/ecjc.4430731211.

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Rezar, Matija, and Fabio Ricciato. "On the impact of time-of-departure knowledge on the accuracy of time-of-arrival localization." Computer Networks 176 (July 2020): 107285. http://dx.doi.org/10.1016/j.comnet.2020.107285.

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Kim, Hye Jin, Cho Hee Lee, and Eun Young Kim. "Temporal differences in eye–hand coordination between children and adults during manual action on objects." Hong Kong Journal of Occupational Therapy 31, no. 2 (December 2018): 106–14. http://dx.doi.org/10.1177/1569186118819892.

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Background/Objective Eye–hand coordination, which is essential for activities of daily living, develops with age. The objective of this study was to investigate the temporal patterns of visual fixation coupled with hands during manual action on objects in children and young adults. Methods Twelve eight-year-old children and 12 young adults performed the Jebsen–Taylor Hand Function Test (JTT) wearing eye-tracking glasses. The interval from the eye arrival time to the hand arrival time on an object was measured as eye–hand arrival span. The interval between the eye departure time and the hand departure time from the object was measured as eye–hand departure span. Eye–hand arrival span, eye–hand departure span and the performance time to complete the JTT were compared between children and young adults. Correlation between eye–hand arrival span and eye–hand departure span was analysed to identify the mechanism of eye–hand coordination. Results Compared with young adults, children showed longer performance time but shorter eye–hand arrival span and eye–hand departure span in the JTT. The difference in mean eye–hand arrival span of overall JTT between children and young adults was significant for both hands, whereas differences in the mean eye–hand departure span on the overall JTT and the total performance time were significant for the non-dominant hand. The eye–hand arrival span was positively correlated with the eye–hand departure span. Conclusion This study demonstrated temporal differences in eye–hand coordination between children and young adults. Temporal patterns of visual fixation coupled to object manipulation could be useful information about the sensorimotor system in the field of occupational therapy.

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Xiao, LingLing, Ronghui Liu, and HaiJun Huang. "Congestion Behavior under Uncertainty on Morning Commute with Preferred Arrival Time Interval." Discrete Dynamics in Nature and Society 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/767851.

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This paper extends the bottleneck model to study congestion behavior of morning commute with flexible work schedule. The proposed model assumes a stochastic bottleneck capacity which follows a uniform distribution and homogeneous commuters who have the same preferred arrival time interval. The commuters are fully aware of the stochastic properties of travel time and schedule delay distributions at all departure times that emerge from day-to-day capacity variations. The commuters’ departure time choice follows user equilibrium (UE) principle in terms of the expected trip cost. Analytical and numerical solutions of this model are provided. The equilibrium departure time patterns are examined which show that the stochastic capacity increases the mean trip cost and lengthens the rush hour. The adoption of flexitime results in less congestion and more efficient use of bottleneck capacity than fixed-time work schedule. The longer the flexi-time interval is, the more uniformly distributed the departure times are.

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Yang, Zhi Yong, and Gui Yun Yan. "Dynamic Model Research of Departure Time and Route Choice Based on Prospect Theory." Advanced Materials Research 243-249 (May 2011): 4418–21. http://dx.doi.org/10.4028/www.scientific.net/amr.243-249.4418.

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This paper takes commuters’ daily travel as research object to build model of travel choice which contains departure time and travel route based on Prospect Theory. Choosing the time of arriving destination as reference point, commuter will choose the time at which he/she can obtain the maximum value as departure time, then establishes choice model of departure time. Using Bayesian Theory to update and adjust route’s forecasting travel time in light of traffic information provided by Advanced Traveler Information Systems (ATIS) and travelers’ previous experience information. Gets decision weighting function after having analyzed traveler’s individual subjective probability which is about the possible result for route choice, then obtains the expression of travel route’s prospect value and gets route choice model. Finally, by designing a network to analyze the dynamic choice model, and achieves expected effect.

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