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Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Models of time travel"

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Roden, David B. "Forecasting Travel Time." Transportation Research Record: Journal of the Transportation Research Board 1518, no. 1 (January 1996): 7–12. http://dx.doi.org/10.1177/0361198196151800102.

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If travel time and speed are to be used as critical performance measures in congestion management systems and air quality analysis procedures, existing modeling techniques will need to be enhanced. Many of the simplifying assumptions that are built into traditional modeling techniques are described. Several relatively simple enhancements to existing models that can greatly improve the model's ability to estimate travel time and speeds are identified, and more advanced methods that could be considered as part of major model redevelopment efforts or detailed air quality studies are suggested. One of these methods involves simulation techniques. The problems and issues of integrating simulation models with travel demand forecasting techniques are outlined, and it is concluded that modeling speed is considerably more difficult than modeling volumes. The bottom-line criterion for any model enhancement is that the procedure supports decision makers in a timely and cost-effective way. This criterion is likely to limit the types of enhancements that are possible.
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Daly, Hannah E., Kalai Ramea, Alessandro Chiodi, Sonia Yeh, Maurizio Gargiulo, and Brian Ó. Gallachóir. "Incorporating travel behaviour and travel time into TIMES energy system models." Applied Energy 135 (December 2014): 429–39. http://dx.doi.org/10.1016/j.apenergy.2014.08.051.

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Carey, Malachy, and Y. E. Ge. "Comparing whole-link travel time models." Transportation Research Part B: Methodological 37, no. 10 (December 2003): 905–26. http://dx.doi.org/10.1016/s0191-2615(02)00091-7.

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4

Yang, Shu, and Yao-Jan Wu. "Mixture Models for Fitting Freeway Travel Time Distributions and Measuring Travel Time Reliability." Transportation Research Record: Journal of the Transportation Research Board 2594, no. 1 (January 2016): 95–106. http://dx.doi.org/10.3141/2594-13.

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Carey, Malachy, Paul Humphreys, Marie McHugh, and Ronan McIvor. "Travel-Time Models With and Without Homogeneity Over Time." Transportation Science 51, no. 3 (August 2017): 882–92. http://dx.doi.org/10.1287/trsc.2016.0674.

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6

van Hinsbergen, C. P. IJ, and J. W. C. van Lint. "Bayesian Combination of Travel Time Prediction Models." Transportation Research Record: Journal of the Transportation Research Board 2064, no. 1 (January 2008): 73–80. http://dx.doi.org/10.3141/2064-10.

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7

Carey, Malachy, and Y. E. Ge. "Efficient Discretisation for Link Travel Time Models." Networks and Spatial Economics 4, no. 3 (September 2004): 269–90. http://dx.doi.org/10.1023/b:nets.0000039783.57975.f0.

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MacGregor Smith, J., and F. R. B. Cruz. "state dependent travel time models and properties." Physica A: Statistical Mechanics and its Applications 395 (February 2014): 560–79. http://dx.doi.org/10.1016/j.physa.2013.10.048.

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Mamdoohi, Amir Reza, Amin Delfan Azari, and Mehrdad Alomoradi. "Estimating Bus Travel Time Using Survival Models." Journal of Planning and Budgeting 24, no. 3 (December 1, 2019): 111–32. http://dx.doi.org/10.29252/jpbud.24.3.111.

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Kuehnel, Nico, Dominik Ziemke, Rolf Moeckel, and Kai Nagel. "The end of travel time matrices: Individual travel times in integrated land use/transport models." Journal of Transport Geography 88 (October 2020): 102862. http://dx.doi.org/10.1016/j.jtrangeo.2020.102862.

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Дисертації з теми "Models of time travel"

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Lu, Chenxi. "Improving Analytical Travel Time Estimation for Transportation Planning Models." FIU Digital Commons, 2010. http://digitalcommons.fiu.edu/etd/237.

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This dissertation aimed to improve travel time estimation for the purpose of transportation planning by developing a travel time estimation method that incorporates the effects of signal timing plans, which were difficult to consider in planning models. For this purpose, an analytical model has been developed. The model parameters were calibrated based on data from CORSIM microscopic simulation, with signal timing plans optimized using the TRANSYT-7F software. Independent variables in the model are link length, free-flow speed, and traffic volumes from the competing turning movements. The developed model has three advantages compared to traditional link-based or node-based models. First, the model considers the influence of signal timing plans for a variety of traffic volume combinations without requiring signal timing information as input. Second, the model describes the non-uniform spatial distribution of delay along a link, this being able to estimate the impacts of queues at different upstream locations of an intersection and attribute delays to a subject link and upstream link. Third, the model shows promise of improving the accuracy of travel time prediction. The mean absolute percentage error (MAPE) of the model is 13% for a set of field data from Minnesota Department of Transportation (MDOT); this is close to the MAPE of uniform delay in the HCM 2000 method (11%). The HCM is the industrial accepted analytical model in the existing literature, but it requires signal timing information as input for calculating delays. The developed model also outperforms the HCM 2000 method for a set of Miami-Dade County data that represent congested traffic conditions, with a MAPE of 29%, compared to 31% of the HCM 2000 method. The advantages of the proposed model make it feasible for application to a large network without the burden of signal timing input, while improving the accuracy of travel time estimation. An assignment model with the developed travel time estimation method has been implemented in a South Florida planning model, which improved assignment results.
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Sikder, Sujan. "Spatial Transferability of Activity-Based Travel Forecasting Models." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4771.

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Spatial transferability of travel forecasting models, or the ability to transfer models from one geographical region to another, can potentially help in significant cost and time savings for regions that cannot invest in extensive data-collection and model-development procedures. This issue is particularly important in the context of tour-based/activity-based models whose development typically involves significant data inputs, skilled staff, and long production times. However, most literature on model transferability has been in the context of traditionally used trip-based models, particularly for linear regression-based trip generation and logit-based mode choice models, with little evidence on the transferability of activity-based models and that of emerging model structures. The overarching goal of this dissertation is to assess the spatial transferability of activity-based travel demand models. To this end, the specific objectives are to: 1. Survey the literature to synthesize: (a) the approaches used to transfer models, (b) the metrics used to assess model transferability, (c) the available evidence on spatial transferability of travel models, and (d) notable gaps in literature; 2. Lay out a framework for assessing the spatial transferability of activity-based travel forecasting model systems, and evaluate alternative methods/metrics used for assessing the transferability of specific model components and their parameters; 3. Conduct empirical assessments of spatial transferability of the following two model components used in today's activity-based model systems: (a) daily activity participation and time-use models, and (b) tour-based time-of-day choice models. Data from the 2009 National Household Travel Survey (NHTS) and the 2000 San Francisco Bay Area Travel Survey (BATS) were used for these empirical assessments; 4. Conduct empirical assessments of model transferability using emerging model structures that have begun to be used in activity-based model systems - specifically the multiple discrete-continuous extreme value (MDCEV) model; 5. Investigate alternate ways of enhancing model transferability; specifically: (a) pooling data from different geographical regions, and (b) improvements to the model structure. The dissertation provides a framework for assessing the transferability of activity-based models systems, along with empirical evidence on the pros and cons of alternative methods and metrics of transferability assessment. The results suggest the need to consider model sensitivity to changes in explanatory variables as opposed to relying solely on the ability to predict aggregate distributions. Updating the constants of a transferred model using local data (a widely used method to transfer models) was found to help in increasing the model's ability to predict aggregate patterns but not necessarily in enhancing its sensitivity to changes in explanatory variables. Also, transferability assessments ought to consider sampling variance in parameter estimates as opposed to only the point estimates. Empirical analysis with the daily activity participation and time-use model shed new light on the prediction properties of the MDCEV model structure that have implications for model transferability. This led to the development of a new model structure called the multiple discrete continuous heteroscedastic extreme value (MDCHEV) model that incorporates heteroscedasticity in the model's stochastic distributions and helps in enhancing model transferability. Transferability assessment of the time-of-day choice models show encouraging evidence of transferability of a large proportion of the model coefficients, albeit except important parameters such as the travel time coefficients. Collectively, there is evidence that pooling data from multiple regions may help in building better transferable models than those transferred from a single region.
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Sidhu, Bobjot Singh. "Exploring Data Driven Models of Transit Travel Time and Delay." DigitalCommons@CalPoly, 2016. https://digitalcommons.calpoly.edu/theses/1601.

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Transit travel time and operating speed influence service attractiveness, operating cost, system efficiency and sustainability. The Tri-County Metropolitan Transportation District of Oregon (TriMet) provides public transportation service in the tri-county Portland metropolitan area. TriMet was one of the first transit agencies to implement a Bus Dispatch System (BDS) as a part of its overall service control and management system. TriMet has had the foresight to fully archive the BDS automatic vehicle location and automatic passenger count data for all bus trips at the stop level since 1997. More recently, the BDS system was upgraded to provide stop-level data plus 5-second resolution bus positions between stops. Rather than relying on prediction tools to determine bus trajectories (including stops and delays) between stops, the higher resolution data presents actual bus positions along each trip. Bus travel speeds and intersection signal/queuing delays may be determined using this newer information. This thesis examines the potential applications of higher resolution transit operations data for a bus route in Portland, Oregon, TriMet Route 14. BDS and 5-second resolution data from all trips during the month of October 2014 are used to determine the impacts and evaluate candidate trip time models. Comparisons are drawn between models and some conclusions are drawn regarding the utility of the higher resolution transit data. In previous research inter-stop models were developed based on the use of average or maximum speed between stops. We know that this does not represent realistic conditions of stopping at a signal/crosswalk or traffic congestion along the link. A new inter-stop trip time model is developed using the 5-second resolution data to determine the number of signals encountered by the bus along the route. The variability in inter-stop time is likely due to the effect of the delay superimposed by signals encountered. This newly developed model resulted in statistically significant results. This type of information is important to transit agencies looking to improve bus running times and reliability. These results, the benefits of archiving higher resolution data to understand bus movement between stops, and future research opportunities are also discussed.
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Jung, Sungyong. "Spatial variability of travel time coefficients in travel demand models and its implication for transportation equilibrium /." The Ohio State University, 1991. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487758680162211.

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Yusuf, Adeel. "Advanced machine learning models for online travel-time prediction on freeways." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50408.

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The objective of the research described in this dissertation is to improve the travel-time prediction process using machine learning methods for the Advanced Traffic In-formation Systems (ATIS). Travel-time prediction has gained significance over the years especially in urban areas due to increasing traffic congestion. The increased demand of the traffic flow has motivated the need for development of improved applications and frameworks, which could alleviate the problems arising due to traffic flow, without the need of addition to the roadway infrastructure. In this thesis, the basic building blocks of the travel-time prediction models are discussed, with a review of the significant prior art. The problem of travel-time prediction was addressed by different perspectives in the past. Mainly the data-driven approach and the traffic flow modeling approach are the two main paths adopted viz. a viz. travel-time prediction from the methodology perspective. This dissertation, works towards the im-provement of the data-driven method. The data-driven model, presented in this dissertation, for the travel-time predic-tion on freeways was based on wavelet packet decomposition and support vector regres-sion (WPSVR), which uses the multi-resolution and equivalent frequency distribution ability of the wavelet transform to train the support vector machines. The results are compared against the classical support vector regression (SVR) method. Our results indi-cate that the wavelet reconstructed coefficients when used as an input to the support vec-tor machine for regression (WPSVR) give better performance (with selected wavelets on-ly), when compared against the support vector regression (without wavelet decomposi-tion). The data used in the model is downloaded from California Department of Trans-portation (Caltrans) of District 12 with a detector density of 2.73, experiencing daily peak hours except most weekends. The data was stored for a period of 214 days accumulated over 5 minute intervals over a distance of 9.13 miles. The results indicate an improvement in accuracy when compared against the classical SVR method. The basic criteria for selection of wavelet basis for preprocessing the inputs of support vector machines are also explored to filter the set of wavelet families for the WDSVR model. Finally, a configuration of travel-time prediction on freeways is present-ed with interchangeable prediction methods along with the details of the Matlab applica-tion used to implement the WPSVR algorithm. The initial results are computed over the set of 42 wavelets. To reduce the compu-tational cost involved in transforming the travel-time data into the set of wavelet packets using all possible mother wavelets available, a methodology of filtering the wavelets is devised, which measures the cross-correlation and redundancy properties of consecutive wavelet transformed values of same frequency band. An alternate configuration of travel-time prediction on freeways using the con-cepts of cloud computation is also presented, which has the ability to interchange the pre-diction modules with an alternate method using the same time-series data. Finally, a graphical user interface is described to connect the Matlab environment with the Caltrans data server for online travel-time prediction using both SVR and WPSVR modules and display the errors and plots of predicted values for both methods. The GUI also has the ability to compute forecast of custom travel-time data in the offline mode.
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Kachani, S. (Soulaymane). "Dynamic travel time models for pricing and route guidance : a fluid dynamics approach." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8527.

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Анотація:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 2002.
Includes bibliographical references (leaves 193-201).
This thesis investigates dynamic phenomena that arise in a variety of systems that share similar characteristics. A common characteristic of particular interest in this work is travel time. We wish to address questions of the type: How long does it take a driver to traverse a route in a transportation network? How long does a unit of product remain in inventory before being sold? As a result, our goal is not only to develop models for travel times as they arise in a variety of dynamically evolving environments, but also to investigate the application of these models in the contexts of dynamic pricing, inventory management, traffic control and route guidance. To address these issues, we develop general models for travel times. To make these models more accessible, we describe them as they apply to transportation systems. We propose first-order and second-order fluid models. We enhance these models to account for spillback and bottleneck phenomena. Based on piecewise linear and piecewise quadratic approximations of the departure or exit flows, we propose several classes of travel time functions. In the area of supply chain, we propose and study a fluid model of pricing and inventory management for make-to-stock manufacturing systems. This model is based on how price and level of inventory affect the time a unit of product remains in inventory. The model applies to non-perishable products. Our motivation is based on the observation that in inventory systems, a unit of product incurs a delay before being sold. This delay depends on the level of inventory of this product, its unit price, and prices of competitors.
(Cont.) The model includes joint pricing, production and inventory decisions in a competitive capacitated multi-product dynamic environment. Finally, we consider the anticipatory route guidance problem, an extension of the dynamic user-equilibrium problem. This problem consists of providing messages to drivers, based on forecasts of traffic conditions, to assist them in their path choice decisions. We propose two equivalent formulations that are the first general analytical formulations of this problem. We establish, under weak assumptions, the existence of a solution to this problem.
by Soulaymane Kachani.
Ph.D.
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7

Emam, Emam. "UTILIZING A REAL LIFE DATA WAREHOUSE TO DEVELOP FREEWAY TRAVEL TIME ELIABILITY STOCHASTIC MODELS." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3987.

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During the 20th century, transportation programs were focused on the development of the basic infrastructure for the transportation networks. In the 21st century, the focus has shifted to management and operations of these networks. Transportation network reliability measure plays an important role in judging the performance of the transportation system and in evaluating the impact of new Intelligent Transportation Systems (ITS) deployment. The measurement of transportation network travel time reliability is imperative for providing travelers with accurate route guidance information. It can be applied to generate the shortest path (or alternative paths) connecting the origins and destinations especially under conditions of varying demands and limited capacities. The measurement of transportation network reliability is a complex issue because it involves both the infrastructure and the behavioral responses of the users. Also, this subject is challenging because there is no single agreed-upon reliability measure. This dissertation developed a new method for estimating the effect of travel demand variation and link capacity degradation on the reliability of a roadway network. The method is applied to a hypothetical roadway network and the results show that both travel time reliability and capacity reliability are consistent measures for reliability of the road network, but each may have a different use. The capacity reliability measure is of special interest to transportation network planners and engineers because it addresses the issue of whether the available network capacity relative to the present or forecast demand is sufficient, whereas travel time reliability is especially interesting for network users. The new travel time reliability method is sensitive to the users' perspective since it reflects that an increase in segment travel time should always result in less travel time reliability. And, it is an indicator of the operational consistency of a facility over an extended period of time. This initial theoretical effort and basic research was followed by applying the new method to the I-4 corridor in Orlando, Florida. This dissertation utilized a real life transportation data warehouse to estimate travel time reliability of the I-4 corridor. Four different travel time stochastic models: Weibull, Exponential, Lognormal, and Normal were tested. Lognormal was the best-fit model. Unlike the mechanical equipments, it is unrealistic that any freeway segment can be traversed in zero seconds no matter how fast the vehicles are. So, an adjustment of the developed best-fit statistical model (Lognormal) location parameter was needed to accurately estimate the travel time reliability. The adjusted model can be used to compute and predict travel time reliability of freeway corridors and report this information in real time to the public through traffic management centers. Compared to existing Florida Method and California Buffer Time Method, the new reliability method showed higher sensitivity to geographical locations, which reflects the level of congestion and bottlenecks. The major advantages/benefits of this new method to practitioners and researchers over the existing methods are its ability to estimate travel time reliability as a function of departure time, and that it treats travel time as a continuous variable that captures the variability experienced by individual travelers over an extended period of time. As such, the new method developed in this dissertation could be utilized in transportation planning and freeway operations for estimating the important travel time reliability measure of performance. Then, the segment length impacts on travel time reliability calculations were investigated utilizing the wealth of data available in the I-4 data warehouse. The developed travel time reliability models showed significant evidence of the relationship between the segment length and the results accuracy. The longer the segment, the less accurate were the travel time reliability estimates. Accordingly, long segments (e.g., 25 miles) are more appropriate for planning purposes as a macroscopic performance measure of the freeway corridor. Short segments (e.g., 5 miles) are more appropriate for the evaluation of freeway operations as a microscopic performance measure. Further, this dissertation has explored the impact of relaxing an important assumption in reliability analysis: Link independency. In real life, assuming that link failures on a road network are statistically independent is dubious. The failure of a link in one particular area does not necessarily result in the complete failure of the neighboring link, but may lead to deterioration of its performance. The "Cause-Based Multimode Model" (CBMM) has been used to address link dependency in communication networks. However, the transferability of this model to transportation networks has not been tested and this approach has not been considered before in the calculation of transportation networks' reliability. This dissertation presented the CBMM and applied it to predict transportation networks' travel time reliability that an origin demand can reach a specified destination under multimodal dependency link failure conditions. The new model studied the multi-state system reliability analysis of transportation networks for which one cannot formulate an "all or nothing" type of failure criterion and in which dependent link failures are considered. The results demonstrated that the newly developed method has true potential and can be easily extended to large-scale networks as long as the data is available. More specifically, the analysis of a hypothetical network showed that the dependency assumption is very important to obtain more reasonable travel time reliability estimates of links, paths, and the entire network. The results showed large discrepancy between the dependency and independency analysis scenarios. Realistic scenarios that considered the dependency assumption were on the safe side, this is important for transportation network decision makers. Also, this could aid travelers in making better choices. In contrast, deceptive information caused by the independency assumption could add to the travelers' anxiety associated with the unknown length of delay. This normally reflects negatively on highway agencies and management of taxpayers' resources.
Ph.D.
Department of Civil and Environmental Engineering
Engineering and Computer Science
Civil Engineering
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Tringides, Constantinos A. "Alternative formulations of joint model systems of departure time choice and mode choice for non-work trips." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000240.

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Nehra, Ram S. "Modeling time space prism constraints in a developing country context." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000299.

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10

Yang, Shu, and Shu Yang. "Estimating Freeway Travel Time Reliability for Traffic Operations and Planning." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/623003.

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Travel time reliability (TTR) has attracted increasing attention in recent years, and is often listed as one of the major roadway performance and service quality measures for both traffic engineers and travelers. Measuring travel time reliability is the first step towards improving travel time reliability, ensuring on-time arrivals, and reducing travel costs. Four components may be primarily considered, including travel time estimation/collection, quantity of travel time selection, probability distribution selection, and TTR measure selection. Travel time is a key transportation performance measure because of its diverse applications and it also serves the foundation of estimating travel time reliability. Various modelling approaches to estimating freeway travel time have been well developed due to widespread installation of intelligent transportation system sensors. However, estimating accurate travel time using existing freeway travel time models is still challenging under congested conditions. Therefore, this study aimed to develop an innovative freeway travel time estimation model based on the General Motors (GM) car-following model. Since the GM model is usually used in a micro-simulation environment, the concepts of virtual leading and virtual following vehicles are proposed to allow the GM model to be used in macro-scale environments using aggregated traffic sensor data. Travel time data collected from three study corridors on I-270 in St. Louis, Missouri was used to verify the estimated travel times produced by the proposed General Motors Travel Time Estimation (GMTTE) model and two existing models, the instantaneous model and the time-slice model. The results showed that the GMTTE model outperformed the two existing models due to lower mean average percentage errors of 1.62% in free-flow conditions and 6.66% in two congested conditions. Overall, the GMTTE model demonstrated its robustness and accuracy for estimating freeway travel times. Most travel time reliability measures are derived directly from continuous probability distributions and applied to the traffic data directly. However, little previous research shows a consensus of probability distribution family selection for travel time reliability. Different probability distribution families could yield different values for the same travel time reliability measure (e.g. standard deviation). It is believe that the specific selection of probability distribution families has few effects on measuring travel time reliability. Therefore, two hypotheses are proposed in hope of accurately measuring travel time reliability. An experiment is designed to prove the two hypotheses. The first hypothesis is proven by conducting the Kolmogorov–Smirnov test and checking log-likelihoods, and Akaike information criterion with a correction for finite sample sizes (AICc) and Bayesian information criterion (BIC) convergences; and the second hypothesis is proven by examining both moment-based and percentile-based travel time reliability measures. The results from the two hypotheses testing suggest that 1) underfitting may cause disagreement in distribution selection, 2) travel time can be precisely fitted using mixture models with higher value of the number of mixture distributions (K), regardless of the distribution family, and 3) the travel time reliability measures are insensitive to the selection of distribution family. Findings of this research allows researchers and practitioners to avoid the work of testing various distributions, and travel time reliability can be more accurately measured using mixture models due to higher value of log-likelihoods. As with travel time collection, the accuracy of the observed travel time and the optimal travel time data quantity should be determined before using the TTR data. The statistical accuracy of TTR measures should be evaluated so that the statistical behavior and belief can be fully understood. More specifically, this issue can be formulated as a question: using a certain amount of travel time data, how accurate is the travel time reliability for a specific freeway corridor, time of day (TOD), and day of week (DOW)? A framework for answering this question has not been proposed in the past. Our study proposes a framework based on bootstrapping to evaluate the accuracy of TTR measures and answer the question. Bootstrapping is a computer-based method for assigning measures of accuracy to multiple types of statistical estimators without requiring a specific probability distribution. Three scenarios representing three traffic flow conditions (free-flow, congestion, and transition) were used to fully understand the accuracy of TTR measures under different traffic conditions. The results of the accuracy measurements primarily showed that: 1) the proposed framework can facilitate assessment of the accuracy of TTR, and 2) stabilization of the TTR measures did not necessarily correspond to statistical accuracy. The findings in our study also suggested that moment-based TTR measures may not be statistically sufficient for measuring freeway TTR. Additionally, our study suggested that 4 or 5 weeks of travel time data is enough for measuring freeway TTR under free-flow conditions, 40 weeks for congested conditions, and 35 weeks for transition conditions. A considerable number of studies have contributed to measuring travel time reliability. Travel time distribution estimation is considered as an important starting input of measuring travel time reliability. Kernel density estimation (KDE) is used to estimate travel time distribution, instead of parametric probability distributions, e.g. Lognormal distribution, the two state models. The Hasofer Lind - Rackwitz Fiessler (HL-RF) algorithm, widely used in the field of reliability engineering, is applied to this work. It is used to compute the reliability index of a system based on its previous performance. The computing procedure for travel time reliability of corridors on a freeway is first introduced. Network travel time reliability is developed afterwards. Given probability distributions estimated by the KDE technique, and an anticipated travel time from travelers, the two equations of the corridor and network travel time reliability can be used to address the question, "How reliable is my perceived travel time?" The definition of travel time reliability is in the sense of "on time performance", and it is conducted inherently from the perspective of travelers. Further, the major advantages of the proposed method are: 1) The proposed method demonstrates an alternative way to estimate travel time distributions when the choice of probability distribution family is still uncertain; 2) the proposed method shows its flexibility for being applied onto different levels of roadways (e.g. individual roadway segment or network). A user-defined anticipated travel time can be input, and travelers can utilize the computed travel time reliability information to plan their trips in advance, in order to better manage trip time, reduce cost, and avoid frustration.
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Книги з теми "Models of time travel"

1

Chen, Huey-Kuo. Dynamic travel choice models: A variational inequality approach. Berlin: Springer, 1999.

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2

Kōtsū no jikan kachi no riron to jissai: Value of travel time : theory and practice. Tōkyō-to Chiyoda-ku: Gihōdō Shuppan, 2013.

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3

Nerhagen, Lena. Travel demand and value of time: Towards an understanding of individuals choice behavior. Gothenburg: Dept. of Economics, Gothenburg University, 2001.

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Jean-Pierre, Maquerlot, and Willems Michèle, eds. Travel and drama in Shakespeare's time. Cambridge [England]: Cambridge University Press, 1996.

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5

Garrow, Laurie A. Discrete choice modelling and air travel demand: Theory and applications. Farnham, Surrey: Ashgate, 2010.

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6

Lapparent, Matthieu de. L' analyse de la valeur du temps dans les déplacements professionnels: De l'approche classique à l'introduction d'incertitude sur les temps de transport. Arcueil: INRETS, 2005.

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7

Mees, Romain M. Locating suppression resources by travel times to wildfires. Berkeley, Calif: U.S. Dept. of Agriculture, Forest Service, Pacific Southwest Forest and Range Experiment Station, 1986.

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8

Evans, Carolyn L. Distance, time, and specialization. Washington, D.C: Federal Reserve Board, 2003.

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9

Evans, Carolyn L. Distance, time, and specialization. Cambridge, Mass: National Bureau of Economic Research, 2003.

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Urban travel demand modeling: From individual choices to general equilibrium. New York: Wiley, 1995.

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Частини книг з теми "Models of time travel"

1

Banister, David. "Time and Travel." In Methods and Models in Transport and Telecommunications, 35–333. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/3-540-28550-4_17.

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Chen, Huey-Kuo. "Dynamic User-Optimal Departure Time/Route Choice Model." In Dynamic Travel Choice Models, 85–102. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-59980-4_6.

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Chen, Huey-Kuo. "Network Flow Constraints and Link Travel Time Function Analysis." In Dynamic Travel Choice Models, 37–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-59980-4_3.

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4

Ran, Bin, and David E. Boyce. "Link Travel Time Functions for Dynamic Network Models." In Lecture Notes in Economics and Mathematical Systems, 337–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-662-00773-0_16.

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Ran, Bin, and David Boyce. "Link Travel Time Functions for Dynamic Network Models." In Modeling Dynamic Transportation Networks, 291–309. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-80230-0_13.

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6

Barceló, Jaume, Xavier Ros-Roca, and Lidia Montero. "Data Analytics and Models for Understanding and Predicting Travel Patterns in Urban Scenarios." In The Evolution of Travel Time Information Systems, 201–77. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89672-0_7.

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7

Holland, Samantha. "‘A Form of Time Travel’: Everyday Vintage." In Modern Vintage Homes & Leisure Lives, 65–91. London: Palgrave Macmillan UK, 2017. http://dx.doi.org/10.1057/978-1-137-57618-7_4.

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Korcek, Pavol, Lukas Sekanina, and Otto Fucik. "Calibration of Traffic Simulation Models Using Vehicle Travel Times." In Lecture Notes in Computer Science, 807–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33350-7_84.

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9

Candela, Rosa, Pietro Michiardi, Maurizio Filippone, and Maria A. Zuluaga. "Model Monitoring and Dynamic Model Selection in Travel Time-Series Forecasting." In Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track, 513–29. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67667-4_31.

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Andersen, Ove, and Kristian Torp. "A Data Model for Determining Weather’s Impact on Travel Time." In Lecture Notes in Computer Science, 437–44. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44406-2_37.

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Тези доповідей конференцій з теми "Models of time travel"

1

Kormaksson, Matthias, Luciano Barbosa, Marcos R. Vieira, and Bianca Zadrozny. "Bus Travel Time Predictions Using Additive Models." In 2014 IEEE International Conference on Data Mining (ICDM). IEEE, 2014. http://dx.doi.org/10.1109/icdm.2014.107.

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Comi, Antonio, Agostino Nuzzolo, Stefano Brinchi, and Renata Verghini. "Bus dispatching irregularity and travel time dispersion." In 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS). IEEE, 2017. http://dx.doi.org/10.1109/mtits.2017.8005632.

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Liu, Zhe, Jiancheng Weng, Qiang Tu, and Ledian Zhang. "Public Transit Based Commuting Travel Time Impact Models." In 18th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2018. http://dx.doi.org/10.1061/9780784481523.090.

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Shen, Bo, and Guojun Chen. "Evaluation of Travel Time Estimation Models with Different Inputs." In Fifth International Conference on Transportation Engineering. Reston, VA: American Society of Civil Engineers, 2015. http://dx.doi.org/10.1061/9780784479384.164.

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5

Narayanan, Aakash Kumar, Chaitra Pranesh, Sarat Chandra Nagavarapu, B. Anil Kumar, and Justin Dauwels. "Data-driven Models for Short-term Travel Time Predictio." In 2019 IEEE Intelligent Transportation Systems Conference - ITSC. IEEE, 2019. http://dx.doi.org/10.1109/itsc.2019.8917456.

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Park, Sangjun, Hesham Rakha, and Feng Guo. "Multi-state travel time reliability model: Impact of incidents on travel time reliability." In 2011 14th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2011). IEEE, 2011. http://dx.doi.org/10.1109/itsc.2011.6082874.

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Bilal, Muhammad Tabish, Samra Sarwar, and Davide Giglio. "Optimization of public transport route assignment via travel time reliability." In 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS). IEEE, 2021. http://dx.doi.org/10.1109/mt-its49943.2021.9529303.

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Mane, Ajinkya S., and Srinivas S. Pulugurtha. "Link-level Travel Time Prediction Using Artificial Neural Network Models." In 2018 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2018. http://dx.doi.org/10.1109/itsc.2018.8569731.

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Liu, Junjuan, Fenyi Dong, and Bingjun Li. "An Inhabitant Travel Time Distribution Model." In Seventh International Conference on Traffic and Transportation Studies (ICTTS) 2010. Reston, VA: American Society of Civil Engineers, 2010. http://dx.doi.org/10.1061/41123(383)57.

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Ghanem, Ahmed, Mohammed Elhenawy, Mohammed Almannaa, Huthaifa I. Ashqar, and Hesham A. Rakha. "Bike share travel time modeling: San Francisco bay area case study." In 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS). IEEE, 2017. http://dx.doi.org/10.1109/mtits.2017.8005582.

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Звіти організацій з теми "Models of time travel"

1

Ballard, Sanford. Analytic solutions for seismic travel time and ray path geometry through simple velocity models. Office of Scientific and Technical Information (OSTI), December 2007. http://dx.doi.org/10.2172/1004383.

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2

Arhin, Stephen, Babin Manandhar, Hamdiat Baba Adam, and Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, April 2021. http://dx.doi.org/10.31979/mti.2021.1943.

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Анотація:
Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) data for six Washington Metropolitan Area Transit Authority (WMATA) bus routes operating in Washington, DC. We developed regression models and Artificial Neural Network (ANN) models for predicting travel times of buses for different peak periods (AM, Mid-Day and PM). Our analysis included variables such as number of served bus stops, length of route between bus stops, average number of passengers in the bus, average dwell time of buses, and number of intersections between bus stops. We obtained ANN models for travel times by using approximation technique incorporating two separate algorithms: Quasi-Newton and Levenberg-Marquardt. The training strategy for neural network models involved feed forward and errorback processes that minimized the generated errors. We also evaluated the models with a Comparison of the Normalized Squared Errors (NSE). From the results, we observed that the travel times of buses and the dwell times at bus stops generally increased over time of the day. We gathered travel time equations for buses for the AM, Mid-Day and PM Peaks. The lowest NSE for the AM, Mid-Day and PM Peak periods corresponded to training processes using Quasi-Newton algorithm, which had 3, 2 and 5 perceptron layers, respectively. These prediction models could be adapted by transit agencies to provide the patrons with accurate travel time information at bus stops or online.
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3

Wenzel, Tom P. Relationship between US Societal Fatality Risk per Vehicle Miles of Travel and Mass, for Individual Vehicle Models over Time (Model Year). Office of Scientific and Technical Information (OSTI), July 2016. http://dx.doi.org/10.2172/1345202.

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4

Arhin, Stephen, Babin Manandhar, Kevin Obike, and Melissa Anderson. Impact of Dedicated Bus Lanes on Intersection Operations and Travel Time Model Development. Mineta Transportation Institute, June 2022. http://dx.doi.org/10.31979/mti.2022.2040.

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Over the years, public transit agencies have been trying to improve their operations by continuously evaluating best practices to better serve patrons. Washington Metropolitan Area Transit Authority (WMATA) oversees the transit bus operations in the Washington Metropolitan Area (District of Columbia, some parts of Maryland and Virginia). One practice attempted by WMATA to improve bus travel time and transit reliability has been the implementation of designated bus lanes (DBLs). The District Department of Transportation (DDOT) implemented a bus priority program on selected corridors in the District of Columbia leading to the installation of red-painted DBLs on corridors of H Street, NW, and I Street, NW. This study evaluates the impacts on the performance of transit buses along with the general traffic performance at intersections on corridors with DBLs installed in Washington, DC by using a “before” and “after” approach. The team utilized non-intrusive video data to perform vehicular turning movement counts to assess the traffic flow and delays (measures of effectiveness) with a traffic simulation software. Furthermore, the team analyzed the Automatic Vehicle Locator (AVL) data provided by WMATA for buses operating on the study segments to evaluate bus travel time. The statistical analysis showed that the vehicles traveling on H Street and I Street (NW) experienced significantly lower delays during both AM (7:00–9:30 AM) and PM (4:00–6:30 PM) peak hours after the installation of bus lanes. The approximation error metrics (normalized squared errors) for the testing dataset was 0.97, indicating that the model was predicting bus travel times based on unknown data with great accuracy. WMATA can apply this research to other segments with busy bus schedules and multiple routes to evaluate the need for DBLs. Neural network models can also be used to approximate bus travel times on segments by simulating scenarios with DBLs to obtain accurate bus travel times. Such implementation could not only improve WMATA’s bus service and reliability but also alleviate general traffic delays.
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Kim, Jinwon, and Jucheol Moon. Congestion Costs and Scheduling Preferences of Car Commuters in California: Estimates Using Big Data. Mineta Transportation Institute, March 2022. http://dx.doi.org/10.31979/mti.2022.2031.

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On average, California car commuters waste 4–5 minutes per morning commute due to congestion. Multiplied across all California car commuters, those few minutes entail a yearly total of approximately 2.3 billion hours of time wasted, costing 6 billion dollars. The objective of this study is to quantify congestion costs and determine how commuters adapt to the level of congestion they face (i.e., commuters’ scheduling utility functions). To that end, this research developed a model of trip scheduling under congestion to construct California commuters’ travel-time profiles, i.e., the menu of travel times that each individual would likely face according to alternate trip timing choices. The results show that commuters facing higher levels of congestion tend to avoid delays by arriving at an inconvenient edge time rather than commuting during the peak. Further, commuters are willing to accept about 0.5 additional minutes of schedule delay to reduce travel time by 1 minute. We found that for most commuters in our data, the travel time profile is much flatter than the estimated schedule utility, which implies that commuters tend to arrive around their own ideal arrival times, although the estimated utility function exhibits a moderate schedule inflexibility. This finding ultimately calls into question the existing bottleneck model’s quantification of the economic cost of congestion as well as the optimal toll to ameliorate congestion.
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Yaremchuk, Olesya. TRAVEL ANTHROPOLOGY IN JOURNALISM: HISTORY AND PRACTICAL METHODS. Ivan Franko National University of Lviv, February 2021. http://dx.doi.org/10.30970/vjo.2021.49.11069.

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Our study’s main object is travel anthropology, the branch of science that studies the history and nature of man, socio-cultural space, social relations, and structures by gathering information during short and long journeys. The publication aims to research the theoretical foundations and genesis of travel anthropology, outline its fundamental principles, and highlight interaction with related sciences. The article’s defining objectives are the analysis of the synthesis of fundamental research approaches in travel anthropology and their implementation in journalism. When we analyze what methods are used by modern authors, also called «cultural observers», we can return to the localization strategy, namely the centering of the culture around a particular place, village, or another spatial object. It is about the participants-observers and how the workplace is limited in space and time and the broader concept of fieldwork. Some disciplinary practices are confused with today’s complex, interactive cultural conjunctures, leading us to think of a laboratory of controlled observations. Indeed, disciplinary approaches have changed since Malinowski’s time. Based on the experience of fieldwork of Svitlana Aleksievich, Katarzyna Kwiatkowska-Moskalewicz, or Malgorzata Reimer, we can conclude that in modern journalism, where the tools of travel anthropology are used, the practical methods of complexity, reflexivity, principles of openness, and semiotics are decisive. Their authors implement both for stable localization and for a prevailing transition.
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7

Kruse, C., Dong Hun Kang, Kenneth Mitchell, Patricia DiJoseph, and Marin Kress. Freight fluidity for the Port of Baltimore : vessel approach and maritime mobility metrics. Engineer Research and Development Center (U.S.), January 2022. http://dx.doi.org/10.21079/11681/43000.

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The United States Army Corps of Engineers is tasked with maintaining waterborne transportation system elements. Understanding channel utilization by vessels informs decisions regarding operations, maintenance, and investments in those elements. Historically, investment decisions have been informed by safety, environmental considerations, and projected economic benefits of alleviating channel restrictions or shipping delays (usually derived from models). However, quantifying causes and impacts of shipping delays based on actual historical vessel location data and then identifying which causes could be ameliorated through investment has been out of reach until recently. In this study, Automatic Identification System vessel position reports were used to develop quantitative measures of transit and dwell-time reliabilities for commercial vessels calling at the Port of Baltimore, Maryland. This port has two deep-water approaches: Chesapeake Bay and the Chesapeake and Delaware Canal. Descriptive metrics were determined for each approach, including port cycle time, harbor stay hours, travel time inbound, and travel time outbound. Then, additional performance measures were calculated: baseline travel time, travel time index, and planning time index. The key finding of this study is that the majority of variability in port cycle time is due to the variability in harbor stay hours, not from channel conditions or channel restrictions.
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Liu, Hongrui, and Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, November 2021. http://dx.doi.org/10.31979/mti.2021.2102.

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In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accidents data consisting of 49 variables describing 4.2 million accident records from February 2016 to December 2020, as well as logistic regression, tree-based techniques such as Decision Tree Classifier and Random Forest Classifier (RF), and Extreme Gradient boosting (XG-boost) to process and train the models. These models will assist people in making smart real-time transportation decisions to improve mobility and reduce accidents.
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Wang, Chih-Hao, and Na Chen. Do Multi-Use-Path Accessibility and Clustering Effect Play a Role in Residents' Choice of Walking and Cycling? Mineta Transportation Institute, June 2021. http://dx.doi.org/10.31979/mti.2021.2011.

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The transportation studies literature recognizes the relationship between accessibility and active travel. However, there is limited research on the specific impact of walking and cycling accessibility to multi-use paths on active travel behavior. Combined with the culture of automobile dependency in the US, this knowledge gap has been making it difficult for policy-makers to encourage walking and cycling mode choices, highlighting the need to promote a walking and cycling culture in cities. In this case, a clustering effect (“you bike, I bike”) can be used as leverage to initiate such a trend. This project contributes to the literature as one of the few published research projects that considers all typical categories of explanatory variables (individual and household socioeconomics, local built environment features, and travel and residential choice attitudes) as well as two new variables (accessibility to multi-use paths calculated by ArcGIS and a clustering effect represented by spatial autocorrelation) at two levels (level 1: binary choice of cycling/waking; level 2: cycling/walking time if yes at level 1) to better understand active travel demand. We use data from the 2012 Utah Travel Survey. At the first level, we use a spatial probit model to identify whether and why Salt Lake City residents walked or cycled. The second level is the development of a spatial autoregressive model for walkers and cyclists to examine what factors affect their travel time when using walking or cycling modes. The results from both levels, obtained while controlling for individual, attitudinal, and built-environment variables, show that accessibility to multi-use paths and a clustering effect (spatial autocorrelation) influence active travel behavior in different ways. Specifically, a cyclist is likely to cycle more when seeing more cyclists around. These findings provide analytical evidence to decision-makers for efficiently evaluating and deciding between plans and policies to enhance active transportation based on the two modeling approaches to assessing travel behavior described above.
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Yu, Haichao, Haoxiang Li, Honghui Shi, Thomas S. Huang, and Gang Hua. Any-Precision Deep Neural Networks. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ejai.v1i1.82.

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We present Any-Precision Deep Neural Networks (Any- Precision DNNs), which are trained with a new method that empowers learned DNNs to be flexible in any numerical precision during inference. The same model in runtime can be flexibly and directly set to different bit-width, by trun- cating the least significant bits, to support dynamic speed and accuracy trade-off. When all layers are set to low- bits, we show that the model achieved accuracy compara- ble to dedicated models trained at the same precision. This nice property facilitates flexible deployment of deep learn- ing models in real-world applications, where in practice trade-offs between model accuracy and runtime efficiency are often sought. Previous literature presents solutions to train models at each individual fixed efficiency/accuracy trade-off point. But how to produce a model flexible in runtime precision is largely unexplored. When the demand of efficiency/accuracy trade-off varies from time to time or even dynamically changes in runtime, it is infeasible to re-train models accordingly, and the storage budget may forbid keeping multiple models. Our proposed framework achieves this flexibility without performance degradation. More importantly, we demonstrate that this achievement is agnostic to model architectures. We experimentally validated our method with different deep network backbones (AlexNet-small, Resnet-20, Resnet-50) on different datasets (SVHN, Cifar-10, ImageNet) and observed consistent results.
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