Vehicles searching for on-street parking create environmental and economic externalities through increasing network traffic flow and congestion, heightening pollutant emission levels, creating additional noise, giving rise to time delays for through vehicles, and leading to potential safety hazards caused by vehicles manoeuvring into or out of on-street spaces. Despite extensive negative impacts on individual drivers and on society, parking search is an under-researched area, particularly in more recent years and within the UK. Furthermore, current statistical modelling techniques applied to parking search time have not utilised a more comprehensive analysis in which hierarchically structured data on multiple levels could be addressed. The aim of this thesis, therefore, is to investigate and compare the factors that influence drivers urban on-street parking search time and its policy implications. A mixed methods approach was applied that comprised qualitative interviews conducted with local government authority Council Officers and a quantitative revealed preference on-street parking survey (sample size, 1,002 observations) undertaken in four cities in the East Midlands region of the UK in order to obtain individual driver-level socio-economic and other parking related factors that may influence parking search time. Statistically significant variables for each of the cities were identified by employing separate linear regression models. A multilevel mixed-effects model in which drivers (Level 1) are nested within streets (Level 2) was then applied to the pooled dataset. Significant factors in the multilevel (street level) model were identified as: time of arrival at a parking place (for which every time period after the 07:00-07:59 reference case indicated increased search time); parking habit; parking tariff; the number of parking places previously visited (on the same trip); trip time from origin to parking place; area type; trip purpose; weather; vehicle type; and walking time from a parking place to a destination. Comparison of the factors that influence parking search time revealed important differences in statistically significant variables and coefficient values between the single-level and multilevel regression modelling approaches. Policy recommendations based upon the findings of the parking survey, modelling analysis, and further interviews conducted with local authority Council Officers, focus around time of arrival at a parking place, area type, parking charges and the potential technological advances that, if implemented, could have a considerable effect on parking search times within urban areas. Robust data collection and subsequent monitoring of parking search activity within each city should be undertaken in order to provide an evidence base which would support the introduction of future policy measures to reduce parking search activity.

Knowledge workers (KW), as important individual agents who embody, exchange, create and exploit knowledge, contribute to regional competitiveness and growth. To attract and retain them in a region, it is necessary to have a better understanding of their fundamental spatially-related behaviors including residence, workplace, and commute choices. In this study, we depart from a perspective of knowledge typology (analytical-synthetic-symbolic knowledge base) to investigate the heterogeneity of knowledge workers' residence, workplace, and commute choices. The case study was conducted in the metropolitan region of Munich. Various types of data are integrated: structural statistical and individually-based web-survey data; individuals' actual choices and their assessment of importance for each criterion; positional and relational data. We find that symbolic Advanced-Producer-Services (APS) workers tend to reside in central areas and use public transport or active modes to commute. In contrast, synthetic high-tech workers are found in relatively peripheral areas and depend more on cars to reach their workplaces. The spatially-related choices of analytical high-tech and synthetic-APS workers are positioned in between symbolic APS-workers and synthetic high-tech workers. We reach three conclusions: Firstly, the features of the knowledge base are evident in the spatial choices of knowledge workers. Secondly, there is a consistency of characteristics between interrelated spaces surrounding residence, workplace, as well as along the commute path of knowledge workers. Lastly, while the influence of the knowledge base has to be weighed against socio-demographic factors, different groups of knowledge workers clearly display distinct choices of residential location and commute mode. These conclusions may provide insights for urban planners and policy-makers regarding the attraction and retention of knowledge workers.

This revealed preference study estimates the implicit value associated with owning a home along a river and tree coverage of riparian areas along rivers. The setting of this study is Ashe and Watauga Counties in Northwestern North Carolina and the two rivers that flow through those counties: New River and Watauga River. House sales form the basis of the hedonic models used to value these environmental characteristics. Homes that border a river sell for at least $28,000 more than otherwise similar homes that do not border a river. Riparian area tree coverage positively impacts river-bordering house prices, but only to a certain point. The results of this study are important for environmental organizations in this region working to safeguard the New and Watauga Rivers through riparian buffer installation and protection. / Master of Science / This study describes homeowner values of owning a home near a river, along with values associated with tree coverage of riparian areas along rivers. The setting of this study is Ashe and Watauga Counties in Northwestern North Carolina and the two rivers that flow through those counties: New River and Watauga River. Using home sales data, models estimate the value of two environmental characteristics home properties. This research found that homes bordering a river sell for at least $28,000 more than otherwise similar homes that do not border a river. Having any amount of tree coverage up to 90% tree coverage in a riparian area increases home sale prices, therefore homeowners positively value tree coverage in riparian areas to a point. Tree coverage in riparian areas is beneficial for the protection of rivers and river-dependent wildlife. The results of this study are important for environmental organizations in this region working to safeguard the New and Watauga Rivers through riparian buffer installation and protection.

Die vorliegende Studienarbeit beschäftigt sich mit der Fragestellung, inwiefern sich Revealed-Preference-Daten aus der deutschen Mobilitätsbefragung "Mobilität in Städten" SrV 2008 dazu eignen, um basierend auf denen im Datensatz enthaltenen Wegen Verkehrsmittelwahlmodelle zu schätzen. Dazu wurden Wegedaten aus der Befragung verwendet, und die Wahlalternativen mit Hilfe der Google Directions API rekonstruiert. Mit den rekonstruierten Variablen Reisezeit und Reisekosten sowie verschiedenen sozioökonomischen und externen Variablen aus SrV 2008 wurden verschiedene Wahlmodelle geschätzt. Durch schrittweises Hinzufügen der Variablen konnte das Modell immer weiter verbessert werden. Wie zu erwarten, erwiesen sich dabei die Reisezeit und die Reisekosten als hoch signifikant. Von den restlichen Variablen waren jedoch lediglich das Geschlecht der befragten Person sowie die Wettersituation zum Zeitpunkt der Wahlentscheidung signifikant. Für das finale Modell wurden Zeitkostensätze errechnet und mit verschiedenen europäischen Studien verglichen. Die errechneten Zeitkostensätze erwiesen sich dabei als plausibel. Die SrV-Daten eignen sich also für die Schätzung von Wahlmodellen. / The following thesis analyzes, if revealed preference data from the German mobility survey "Mobilität in Städten" SrV 2008 is suited to estimate mode choice models. For that purpose, trip data from the survey was used and the different choice alternatives were reconstructed with the Google Directions API. Several mode choice models were estimated with the help of the reconstructed variables travel time and travel costs plus several socioeconomic and external variables from SrV 2008. The variables were added to the model step by step, thereby the quality of the model improved. As expected, travel time and travel costs were highly significant. However from the remaining variables only the gender of the person and the weather at the time of the trip were significant. For the final model, values of time were calculated and these were compared with values from different European studies. The calculated values of time proved to be feasible. Therefore, SrV data is suited to be used for mode choice models.

Die vorliegende Studienarbeit beschäftigt sich mit der Fragestellung, inwiefern sich Revealed-Preference-Daten aus der deutschen Mobilitätsbefragung "Mobilität in Städten" SrV 2008 dazu eignen, um basierend auf denen im Datensatz enthaltenen Wegen Verkehrsmittelwahlmodelle zu schätzen. Dazu wurden Wegedaten aus der Befragung verwendet, und die Wahlalternativen mit Hilfe der Google Directions API rekonstruiert. Mit den rekonstruierten Variablen Reisezeit und Reisekosten sowie verschiedenen sozioökonomischen und externen Variablen aus SrV 2008 wurden verschiedene Wahlmodelle geschätzt. Durch schrittweises Hinzufügen der Variablen konnte das Modell immer weiter verbessert werden. Wie zu erwarten, erwiesen sich dabei die Reisezeit und die Reisekosten als hoch signifikant. Von den restlichen Variablen waren jedoch lediglich das Geschlecht der befragten Person sowie die Wettersituation zum Zeitpunkt der Wahlentscheidung signifikant. Für das finale Modell wurden Zeitkostensätze errechnet und mit verschiedenen europäischen Studien verglichen. Die errechneten Zeitkostensätze erwiesen sich dabei als plausibel. Die SrV-Daten eignen sich also für die Schätzung von Wahlmodellen.:1 Einleitung 1 1.1 Vorstellung des Themas 1 1.2 Ziel dieser Arbeit 1 1.3 Gliederung dieser Arbeit 2 1.4 Wesentliche Ergebnisse 2 2 Theoretischer Hintergrund 3 2.1 Diskrete Wahltheorie 3 2.1.1 Deterministischer Nutzen 4 2.1.2 Stochastischer Störterm 5 2.1.3 Logit-Modell 6 2.2 Parameterschätzung 8 2.2.1 t-Test 9 2.2.2 Likelihood-Ratio Test 9 2.2.3 Likelihood-Ratio-Index 10 2.3 Datenquellen 10 3 Generierung eines RP-Datensatzes aus SrV-Daten 13 3.1 SrV 2008 13 3.2 Auswahl an Variablen und Datensätzen 13 3.3 Rekonstruktion der generischen Variablen 15 3.3.1 Google Directions API 15 3.3.2 Automatisierte API-Abfrage 16 3.3.3 Reisekosten 17 3.4 Erzeugter Datensatz 18 4 Modellentwicklung und Parameterschätzung 21 4.1 Entwicklung des Wahlmodells 21 4.2 Parameterschätzung mit Biogeme 24 4.3 Anwendung der Parameterschätzung auf die SrV-Daten 28 5 Diskussion der Modellergebnisse 33 5.1 Darstellung der Nutzeneinflüsse 33 5.2 Zeitkostensätze 35 5.3 Fehlerquellen 37 5.4 Fazit 38 Literaturverzeichnis 41 Datenquellen 45 Anhang 49 / The following thesis analyzes, if revealed preference data from the German mobility survey "Mobilität in Städten" SrV 2008 is suited to estimate mode choice models. For that purpose, trip data from the survey was used and the different choice alternatives were reconstructed with the Google Directions API. Several mode choice models were estimated with the help of the reconstructed variables travel time and travel costs plus several socioeconomic and external variables from SrV 2008. The variables were added to the model step by step, thereby the quality of the model improved. As expected, travel time and travel costs were highly significant. However from the remaining variables only the gender of the person and the weather at the time of the trip were significant. For the final model, values of time were calculated and these were compared with values from different European studies. The calculated values of time proved to be feasible. Therefore, SrV data is suited to be used for mode choice models.:1 Einleitung 1 1.1 Vorstellung des Themas 1 1.2 Ziel dieser Arbeit 1 1.3 Gliederung dieser Arbeit 2 1.4 Wesentliche Ergebnisse 2 2 Theoretischer Hintergrund 3 2.1 Diskrete Wahltheorie 3 2.1.1 Deterministischer Nutzen 4 2.1.2 Stochastischer Störterm 5 2.1.3 Logit-Modell 6 2.2 Parameterschätzung 8 2.2.1 t-Test 9 2.2.2 Likelihood-Ratio Test 9 2.2.3 Likelihood-Ratio-Index 10 2.3 Datenquellen 10 3 Generierung eines RP-Datensatzes aus SrV-Daten 13 3.1 SrV 2008 13 3.2 Auswahl an Variablen und Datensätzen 13 3.3 Rekonstruktion der generischen Variablen 15 3.3.1 Google Directions API 15 3.3.2 Automatisierte API-Abfrage 16 3.3.3 Reisekosten 17 3.4 Erzeugter Datensatz 18 4 Modellentwicklung und Parameterschätzung 21 4.1 Entwicklung des Wahlmodells 21 4.2 Parameterschätzung mit Biogeme 24 4.3 Anwendung der Parameterschätzung auf die SrV-Daten 28 5 Diskussion der Modellergebnisse 33 5.1 Darstellung der Nutzeneinflüsse 33 5.2 Zeitkostensätze 35 5.3 Fehlerquellen 37 5.4 Fazit 38 Literaturverzeichnis 41 Datenquellen 45 Anhang 49

This research examined if travelers are paying for travel on managed lanes (MLs) as they indicated that they would in a 2008 survey. The other objectives of this research included estimating travelers’ value of travel time savings (VTTS) and their value of travel time reliability (VOR), and examining the multiple survey designs used in a 2008 survey to identify which survey design better predicted ML traveler behavior. To achieve the objectives, an Internet-based follow-up stated preference (SP) survey of Houston’s Katy Freeway travelers was conducted in 2010. Three survey design methodologies—Db-efficient, random level generation, and adaptive random—were tested in this survey. A total of 3,325 responses were gathered from the survey, and of those, 869 responses were from those who likely also responded to the previous 2008 survey. Mixed logit models were developed for those 869 previous survey respondents to estimate and compare the VTTS to the 2008 survey estimates. It was found that the 2008 survey estimates of the VTTS were very close to the 2010 survey estimates. In addition, separate mixed logit models were developed from the responses obtained from the three different design strategies in the 2010 survey. The implied mean VTTS varied across the design-specific models. Only the Db-efficient design was able to estimate a VOR. Based on this and several other metrics, the Db-efficient design outperformed the other designs. A mixed logit model including all the responses from all three designs was also developed; the implied mean VTTS was estimated as 65 percent ($22/hr) of the mean hourly wage rate, and the implied mean VOR was estimated as 108 percent ($37/hr) of the mean hourly wage rate. Data on actual usage of the MLs were also collected. Based on actual usage, the average VTTS was calculated as $51/hr. However, the $51/hr travelers are paying likely also includes the value travelers place on travel time reliability of the MLs. The total (VTTS+VOR) amount estimated from the all-inclusive model from the survey was $59/hr, which is close to the value estimated from the actual usage. The Db-efficient design estimated this total as $50/hr. This research also shows that travelers have a difficulty in estimating the time they save while using a ML. They greatly overestimate the amount of time saved. It may well be that even though travelers are saving a small amount of time they value that time savings (and avoiding congestion) much higher – possibly similar to their amount of perceived travel time savings. The initial findings from this study, reported here, are consistent with the hypothesis that travelers are paying for their travel on MLs, much as they said that they would in our previous survey. This supports the use of data on intended behavior in policy analysis.

Submitted by Secretaria Pós de Produção (tpp@vm.uff.br) on 2017-07-27T18:53:19Z No. of bitstreams: 1 D2014 - Thiago Graça Ramos.pdf: 589803 bytes, checksum: d74ab5e26ec9908670c7d3320d45fe61 (MD5) / Made available in DSpace on 2017-07-27T18:53:19Z (GMT). No. of bitstreams: 1 D2014 - Thiago Graça Ramos.pdf: 589803 bytes, checksum: d74ab5e26ec9908670c7d3320d45fe61 (MD5) / Esse estudo visa construir um modelo para identificar a forma ideal de transporte de carga no Brasil, para pequenas e médias empresas que contratam este tipo de serviço. O trabalho utilizou as técnicas DEA, preferência declarada e logito ordinal para avaliar as pequenas e médias empresas que contratam transporte de carga no Brasil, verificando os aspectos importantes para a tomada de decisão na contratação deste serviço. Inicialmente, aplicou-se a ferramenta DEA para classificar as eficiências em alta, média e baixa, utilizandose o resultado de tal classificação como a variável dependente do modelo logito ordinal. As variáveis independentes deste modelo foram as utilidades oriundas da preferência declarada e do modelo de MaxDiff, que avaliou características não pertencentes ao modelo de preferência declarada. A análise dos dados indicou que a migração do modo rodoviário para o ferroviário seria melhor para as empresas, já que o primeiro acaba sendo utilizado pela falta de opção pelo segundo. Outro importante resultado do estudo foi a indicação de que as empresas com produtos de maior valor agregado são mais eficientes. Por fim, o modelo indicou que o modo de operação a ser buscado pelas empresas de transporte de carga deve incluir segurança e rapidez na entrega, propiciando facilidade de acesso ao consumidor. / This paper aims to identify efficient businesses in daily freight transport and to evaluate the main aspects to picking and hiring a cargo transportation service. To make this evaluation, some techniques will be used, such as Data Envelopment Analysis, ordinal logit and revealed preference. By using the DEA technique, the efficiency will be ranked between high, medium and low, and this ranking will be the dependent variable of the ordinal logit model, and the independent variables of this model are derived from the utilities from the revealed preference model and the maxdiff model that evaluated some features that were not declared on the preference model. Data analysis indicated that the migration from road to rail would be better for companies since the first ends up being used by a lack of options for the second. Another important result was the indication that firms with higher value-added products are more efficient. Finally, the model indicated that the mode of operation being sought by cargo shipping companies should include safety and speed in delivery, providing easy access to the consumer.

Travel demand forecasting is essential for many decisions, such as infrastructure investments and policy measures. Traditionally travel demand modelling has considered trip frequency, mode, destination and route choice. This thesis considers two other choice dimensions, hypothesised to have implications for travel demand forecasting. The first part investigates how the increased possibilities to overcome space that ICT (information and communication technology) provides, can be integrated in travel demand forecasting models. We find that possibilities of modelling substitution effects are limited, irrespective of data source and modelling approach. Telecommuting explains, however, a very small part of variation in work trip frequency. It is therefore not urgent to include effects from telecommuting in travel demand forecasting. The results indicate that telecommuting is a privilege for certain groups of employees, and we therefore expect that negative attitudes from management, job suitability and lack of equipment are important obstacles. We find also that company benefits can be obtained from telecommuting. No evidences that telecommuting gives rise to urban sprawl is, however, found. Hence, there is ground for promoting telecommuting from a societal, individual and company perspective. The second part develops a departure time choice model in a mixed logit framework. This model explains how travellers trade-off travel time, travel time variability, monetary and scheduling costs, when choosing departure time. We explicitly account for correlation in unobserved heterogeneity over repeated SP choices, which was fundamental for accurate estimation of the substitution pattern. Temporal constraints at destination are found to mainly restrict late arrival. Constraints at origin mainly restrict early departure. Sensitivity to travel time uncertainty depends on trip type and intended arrival time. Given appropriate input data and a calibrated dynamic assignment model, the model can be applied to forecast peak-spreading effects in congested networks. Combined stated preference (SP) and revealed preference (RP) data is used, which has provided an opportunity to compare observed and stated behaviour. Such analysis has previously not been carried out and indicates that there are systematic differences in RP and SP data. / QC 20100825

AgÃncia Nacional do PetrÃleo / A utilizaÃÃo de modelos de escolha discreta à um mÃtodo eficaz que retrata o comportamento dos consumidores em diversos mercados. Sua aplicaÃÃo tem sido amplamente difundida na literatura para retratar a realidade de mercados de produtos e serviÃos no setor de transportes. Quando sÃo necessÃrios estudos de previsÃo de demanda, o modo mais adequado consiste na utilizaÃÃo conjunta de dados de preferÃncia declarada (PD) e preferÃncia revelada (PR). A combinaÃÃo destes dados fornece modelos estatisticamente mais consistentes do que aqueles estimados com dados puros de PD ou de PR. Dessa forma, o presente trabalho aplica uma metodologia baseada em modelos de escolha discreta com insumo de dados de PD e PR, chamada de GNVPREV, para analisar as preferÃncias dos usuÃrios de combustÃveis, enfocando o gÃs natural veicular - GNV, dentro de um contexto competitivo de um mercado de energÃticos veiculares. Esta anÃlise foi restrita aos usuÃrios de veÃculos leves - automÃveis, camionetas e caminhonetes - que utilizam a gasolina, o Ãlcool ou o prÃprio GNV. A metodologia GNVPREV foi aplicada para uma Ãrea de estudo constituÃda por uma parte do distrito sede do municÃpio de Caucaia, situado na RegiÃo Metropolitana de Fortaleza, Estado do CearÃ. O levantamento de dados a partir de questionÃrios de preferÃncia declarada e revelada, elaborados previamente, forneceu insumos para estimativas de funÃÃes de utilidade e obtenÃÃo de parÃmetros de elasticidade de demanda, trade-off entre alternativas e cenÃrios de previsÃo de demanda. Os resultados obtidos foram satisfatÃrios, dentro das limitaÃÃes dos dados primÃrios e secundÃrios, e confirmaram um melhor desempenho do modelo quando estimado com dados conjuntos de PD e PR.

Travel demand forecasting is essential for many decisions, such as infrastructure investments and policy measures. Traditionally travel demand modelling has considered trip frequency, mode, destination and route choice. This thesis considers two other choice dimensions, hypothesised to have implications for travel demand forecasting. The first part investigates how the increased possibilities to overcome space that ICT (information and communication technology) provides, can be integrated in travel demand forecasting models. We find that possibilities of modelling substitution effects are limited, irrespective of data source and modelling approach. Telecommuting explains, however, a very small part of variation in work trip frequency. It is therefore not urgent to include effects from telecommuting in travel demand forecasting. The results indicate that telecommuting is a privilege for certain groups of employees, and we therefore expect that negative attitudes from management, job suitability and lack of equipment are important obstacles. We find also that company benefits can be obtained from telecommuting. No evidences that telecommuting gives rise to urban sprawl is, however, found. Hence, there is ground for promoting telecommuting from a societal, individual and company perspective. The second part develops a departure time choice model in a mixed logit framework. This model explains how travellers trade-off travel time, travel time variability, monetary and scheduling costs, when choosing departure time. We explicitly account for correlation in unobserved heterogeneity over repeated SP choices, which was fundamental for accurate estimation of the substitution pattern. Temporal constraints at destination are found to mainly restrict late arrival. Constraints at origin mainly restrict early departure. Sensitivity to travel time uncertainty depends on trip type and intended arrival time. Given appropriate input data and a calibrated dynamic assignment model, the model can be applied to forecast peak-spreading effects in congested networks. Combined stated preference (SP) and revealed preference (RP) data is used, which has provided an opportunity to compare observed and stated behaviour. Such analysis has previously not been carried out and indicates that there are systematic differences in RP and SP data. / QC 20100825