El objetivo de esta investigación es proponer una mejora en la planificación del transporte y viabilización de proyectos hospitalarios Categoría II-2 localizados en la ciudad de Lima Metropolitana mediante el uso de tasas de generación de viajes locales. Gracias a la recolección de información, estudios de demanda, análisis estadísticos y el uso de la metodología propuesta por el Institute of Transportation Enginnering (ITE) se logra conocer la relación que existe entre los viajes realizados al polo generador de viajes (PGV) y las características que este posee. Se utilizaron cuatro hospitales de la misma categoría en diversos puntos de la ciudad para poder establecer dichas tasas, se recolectó información de los mismos centros hospitalarios cuyos valores son usados como variables independientes del presente estudio, por ejemplo: el área de terreno, área construida, número de camas, entre otros. Además de calcular la tasa de generación de viajes, también se determinó la ecuación de regresión lineal o logarítmica, se graficaron las curvas de tendencia y se establecieron las variables independientes que influyen principalmente al presente PGV, luego de calcular el coeficiente de determinación (R2) que más se ajusta y aplicando la prueba de hipótesis nula, que demuestra estadísticamente que la correlación entre la variable dependiente y la independiente es aceptable con un nivel de confianza del 95%. Al finalizar el estudio, se comprueba que existe una variación en los resultados locales entre el 78% y 84% respecto a los del ITE. / The objective of this research is to propose an improvement in the transportation planning and viability of Category II-2 hospital projects located in the city of Metropolitan Lima through the use of local travel generation rates. Thanks to the collection of information, demand studies, statistical analysis and the use of the methodology proposed by the Institute of Transportation Enginnering (ITE), it is possible to know the relationship between trips made to the trip generator pole (PGV) and the characteristics it has. Four hospitals of the same category were used in different points of the city to establish these rates, information was collected from the same hospital centers whose values are used as independent variables of the present study, for example: the land area, built area, number of beds, among others. In addition to calculating the travel generation rate, the linear or logarithmic regression equation was also determined, the trend curves were plotted and the independent variables that mainly influence the present PGV were established, after calculating the coefficient of determination (R2) The best fit and applying the null hypothesis test, which statistically shows that the correlation between the dependent and the independent variable is acceptable with a confidence level of 95%. At the end of the study, it is verified that there is a variation in local results between 78% and 84% with respect to those of the ITE. / Tesis

Recent research has shown that incorporating roadway safety in transportation planning has been considered one of the active approaches to improve safety. Aggregate level analysis for predicting crash frequencies had been contemplated to be an important step in this process. As seen from the previous studies various categories of predictors at macro level (census blocks, traffic analysis zones, census tracts, wards, counties and states) have been exhausted to find appropriate correlation with crashes. This study contributes to this ongoing macro level road safety research by investigating various trip productions and attractions along with roadway characteristics within traffic analysis zones (TAZs) of four counties in the state of Florida. Crashes occurring in one thousand three hundred and forty-nine TAZs in Hillsborough, Citrus, Pasco, and Hernando counties during the years 2005 and 2006 were examined in this study. Selected counties were representative from both urban and rural environments. To understand the prevalence of various trip attraction and production rates per TAZ the Euclidian distances between the centroid of a TAZ containing a particular crash and the centroid of the ZIP area containing the at fault driver's home address for that particular crash was calculated. It was found that almost all crashes in Hernando and Citrus County for the years 2005-2006 took place in about 27 miles radius centering at the at-fault drivers' home. Also about sixty-two percent of crashes occurred approximately at a distance of between 2 and 10 miles from the homes of drivers who were at fault in those crashes. These results gave an indication that home based trips may be more associated with crashes and later trip related model estimates which were found significant at 95% confidence level complied with this hypothesized idea. Previous aggregate level road safety studies widely addressed negative binomial distribution of crashes. Properties like non-negative integer counts, non-normal distribution, over-dispersion in the data have increased suitability of applying the negative binomial technique and has been selected to build crash prediction models in this research. Four response variables which were aggregated at TAZ-level were total number of crashes, severe (fatal and severe injury) crashes, total crashes during peak hours, and pedestrian and bicycle related crashes. For each response separate models were estimated using four different sets of predictors which are i) various trip variables, ii) total trip production and total trip attraction, iii) road characteristics, and iv) finally considering all predictors into the model. It was found that the total crash model and peak hour crash model were best estimated by the total trip productions and total trip attractions. On the basis of log-likelihoods, deviance value/degree of freedom, and Pearson Chi-square value/degree of freedom, the severe crash model was best fit by the trip related variables only and pedestrian and bicycle related crash model was best fit by the road related variables only. The significant trip related variables in the severe crash models were home-based work attractions, home-based shop attractions, light truck productions, heavy truck productions, and external-internal attractions. Only two variables- sum of roadway segment lengths with 35 mph speed limit and number of intersections per TAZ were found significant for pedestrian and bicycle related crash model developed using road characteristics only. The 1349 TAZs were grouped into three different clusters based on the quartile distribution of the trip generations and were termed as less-tripped, moderately-tripped, and highly-tripped TAZs. It was hypothesized that separate models developed for these clusters would provide a better fit as the clustering process increases the homogeneity within a cluster. The cluster models were re-run using the significant predictors attained from the joint models and were compared with the previous sets of models. However, the differences in the model fits (in terms of Alkaike's Information Criterion values) were not significant. This study points to different approaches when predicting crashes at the zonal level. This research is thought to add to the literature on macro level crash modeling research by considering various trip related data into account as previous studies in zone level safety have not explicitly considered trip data as explanatory covariates.

<p>In today’s fast-paced data-driven world, accumulating and organizing streams of high-resolution information plays a vital role in numerous decision and design tasks. The transportation sector is a prime example of this. Fine-scale information on traffic exposure at specific observation periods is critical to the successful analysis of road safety. Annual Average Daily Traffic (AADT) and hourly traffic volumes represent essential statistics to predict crash risk under time-dependent conditions, such as, weather and seasonal traffic variations. State highway agencies including the Indiana Department of Transportation (INDOT) collect traffic count data using multiple permanent and coverage count stations. However, approximately ten percent of the local-administered road segments in Indiana are included in their database. To impute the missing data, predictive models that can accurately forecast AADT and consequently, hourly traffic volumes, will be of great value.</p> <p><br></p> <p>To address this problem, this thesis proposes a methodology to predict traffic volumes in different classes of urban road segments in Indiana. Two sets of regression models have been developed: (1) AADT Estimation Model, and (2) Hourly Traffic Volume Model. These models include effects of spatial and temporal variations, land use, roadway characteristics and, previously-overlooked in such models, road network connectivity and route selection. These, in turn, address two important research questions: (1) how trips are generated and (2) how people choose routes. The spatial and temporal effects that were considered in the analysis are travel propensity, travel time excess index, road class, hour of day, day of week and seasonal variations. While travel propensity captures particulars of network connectivity and land-use characteristics in traffic analysis zones (TAZ), the travel time excess index accounts for commuters’ route-choice. The estimation results indicate that all these variables are strongly correlated with traffic volumes on considered roadways. Reasonable estimations of hourly traffic volumes on a network scale can be achieved using the proposed model. In addition to aiding safety management at disaggregate level, hourly traffic predictions can help highway agencies in other system-wide analysis where such traffic information is needed.</p>

No description available.

No description available.

published_or_final_version / abstract / Urban Planning and Environmental Management / Doctoral / Doctor of Philosophy

published_or_final_version / Geography / Doctoral / Doctor of Philosophy

Dans la pratique de la planification urbaine, la prévision de la demande de transport fait en général appel au modèle à quatre étapes (génération, distribution, répartition modale et affectation), malgré des avancées théoriques considérables dans le domaine. Cette persistance s’explique par une facilité relative de mise en oeuvre, liée notamment à la forme des données disponibles et susceptibles d’alimenter les modèles. Cependant, la nature statique de l’approche pose des interrogations quant à sa pertinence pour faire des prévisions de moyen-long terme. Cette thèse étudie, la validité de l’hypothèse de stabilité temporelle des trois premières étapes du modèle de prévision. Pour ce faire, en prenant l’agglomération lyonnaise comme terrain d’étude, nous avons codifié des réseaux routiers et de transports en commun à différentes dates (1985, 1995 et 2006). Cette donne, généralement indisponible, combinée aux enquêtes ménages déplacements correspondantes, nous permet de calibrer les trois premières étapes du modèle traditionnel et de tester leur capacité prédictive. Pour les modèles de génération, on note des prévisions acceptables à un horizon de 10 ans. À 20 ans, certaines évolutions dans les styles de vie se sont traduites par une baisse du nombre moyen de sorties pour le motif travail, que les modèles traditionnels ne permettent pas de prévoir complètement. Au niveau de la distribution, l’allongement des distances entre lieux de réalisation de certaines activités et le lieu de domicile peut être relativement bien reproduit par des modèles gravitaires avec des paramètres stables dans le temps. Au niveau de la répartition modale, les paramètres ne sont pas stables et les modèles estimés n’auraient pas permis de prévoir le regain de parts de marché des transports en commun observé ces dernières années. / In the practice of urban planning, travel demand forecasts are generally obtained by using the four-step model (generation, distribution, modal split and assignment), despite considerable theoretical advances in the field. This persistence can be explained by the relative ease of implementation of the four-step modelling sequence, which is related, in particular, to the kind of data available that could be used as an input in a model. However, the static nature of the approach raises questions as it pertains to its relevance in producing medium and long range forecasts. This thesis investigates the validity of the hypothesis of temporal stability of the parameters of the first three stages of the traditional forecasting sequence. To do this, taking the Lyon conurbation as our case study, we coded the road and transit networks at different points in time (1985, 1995 and 2006). We then combine this temporal data, which is generally unavailable, with the corresponding household travel surveys in order to calibrate the first three steps of the traditional model and test their predictive ability. For the generation models tested, we note acceptable performance for a 10-year forecast. For a 20-year forecast, some changes in lifestyles have resulted in a decrease in the average number of work trips that traditional models do not predict accurately. Regarding trip distribution, the increase in travel distances observed for certain purposes is reproduced fairly well by the gravity model. At the modal split level, the parameters are not stable and the estimated models would be unable to predict accurately the recent increase in the market share of public transport.

The purpose of this research is to develop and test a widely available, ready-to-use method for adjusting the Institute of Transportation Engineers (ITE) Trip Generation Handbook vehicle trip generation estimates for urban context using regional household travel survey data. The ITE Handbook has become the predominant method for estimating vehicle trips generated by different land uses or establishment, providing a method for data collection and vehicle trip estimation based on the size of the development (e.g. gross square footage, number of employees, number of dwelling units). These estimates are used in traffic impact analysis to assess the amount of impact the development will have on nearby transportation facilities and, the corresponding charges for mitigating the development's negative impacts, with roadway expansions, added turning bays, additional parking or traffic signalization, for example. The Handbook is often criticized, however, for its inability to account for variations in travel modes across urban contexts. For more than fifty years, ITE has collected suburban, vehicle-oriented data on trip generation for automobiles only. Despite the provision of warnings against application in urban areas, local governments continue to require the use of the ITE Handbook across all area-types. By over predicting vehicle traffic to developments in urban developments, developments may be overcharged to mitigate these developments locating in urban environments despite the lower automobile mode shares, discouraging infill development or densification. When ITE's Trip Generation Handbook overestimates the vehicle impact of a development, facilities are also overbuilt for the automobile traffic and diminishing the use of alternative modes. When ITE's TGH underestimates this impact, adjacent facilities may become oversaturated with traffic, pushing cars onto smaller facilities nearby. Currently, there is momentum amongst practitioners to improve these estimation techniques in urban contexts to help support smart growth and better plan for multiple modes. This research developed and tested a method to adjust ITE's Handbook vehicle trip generation estimates for changes in transportation mode shares in more urban contexts using information from household travel surveys. Mode share adjustments provide direct reductions to ITE's Handbook vehicle trip estimations. Household travel survey (HTS) data from three regions were collected: Portland, Oregon; Seattle, Washington; and Baltimore, Maryland. These data were used to estimate the automobile mode share rates across urban context using three different adjustment methodologies: (A) a descriptive table of mode shares across activity density ranges, (B) a binary logistic regression that includes a built environment description of urban context with the best predictive power, and (C) a binary logistic regression that includes a built environment description of urban context with high predictive power and land use policy-sensitivity. Each of these three methods for estimating the automobile mode share across urban context were estimated for each of nine land use categories, resulting in nine descriptive tables (Adjustment A) and eighteen regressions (Adjustments B and C). Additionally, a linear regression was estimated to predict vehicle occupancy rates across urban contexts for each of nine land use categories. 195 independently collected establishment-level vehicle trip generation data were collected in accordance with the ITE Handbook to validate and compare the performance of the three adjustment methods and estimations from the Handbook. Six land use categories (out of the nine estimated) were able to be tested. Out of all of the land uses tested and verified, ITE's Trip Generation Handbook appeared to have more accurate estimations for land uses that included residential condominiums/townhouses (LUC 230), supermarkets (LUC 850) and quality (sit-down) restaurants (LUC 931). Moderate or small improvements were observed when applying urban context adjustments to mid-rise apartments (LUC 223), high-turnover (sit-down) restaurants (LUC 932). The most substantial improvements occurred at high-rise apartments (LUC 222) and condominiums/townhouses (LUC 232), shopping centers (LUC 820), or coffee/donut (LUC 936) or bread/donut/bagel shops (LUC 939) without drive-through windows. The three methods proposed to estimate automobile mode share provides improvements to the Handbook rates for most infill developments in urban environments. For the land uses analyzed, it appeared a descriptive table of mode shares across activity density provided results with comparable improvements to the results from the more sophisticated binary logistic model estimations. Additional independently collected establishment-level data collections representing more land uses, time periods and time of days are necessary to determine how ITE's Handbook performs in other circumstances, including assessing the transferability of the vehicle trip end rates or mode share reductions across regions.

Transportation planners are often faced with the problem of estimating passenger and freight flows between regions. In the literature there are many models for passenger flows. However, models about freight flows are more limited. Modeling freight flow is also more complex than modeling passenger flow and there are many agents related with freight flows. In addition, data availability is a critical factor. In this research, freight flows between provinces in T&uuml / rkiye are forecasted by demand analysis. Transportation is one of the important activities of human beings and plays an important role for spatial interactions in economic growth. In other words, there is a very strong linkage between economic growth and the freight flow, thus transportation demand. Regional trade as spatial flow appears on transportation systems as freight flows. In this study, using the existing limited data and surveys in T&uuml / rkiye, nationwide origin-destination (O-D) matrix of freight flows between provinces is obtained. Using this empirical matrix, the generation of freight flows of provinces is formulated depending on the socioeconomic and demographic variables by means of multiple linear regression analysis. In addition, interactions of freight flows between provinces and economic growth of regions are investigated. The generations and attractions of provinces as freight flow are distributed between provinces with traditional gravity model. By comparing observed O-D matrix and simulated O-D matrix, gravity model is calibrated. Calibration is also performed by freight trip length distribution. In this research, two steps of traditional &ldquo / four-step analysis&rdquo / , &ldquo / trip generation&rdquo / and &ldquo / trip distribution&rdquo / , are applied to develop nationwide freight demand model between the provinces in T&uuml / rkiye. The developed model is single-mode, single commodity and nationwide.