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... n is the number of observations, is the estimation of the standard deviation of the residuals, t r ( S ) is the trace of hat matrix. The AICc shows the information distance between the ‘true’ and the fitted model. This is a relative distance also known as Kullback-Leibler information distance (26) . The lower the AIC is, the closer the fitted model is to the true. A series of tests has been suggested to check if spatial non-stationarity exists when applying a GWR. Leung et al. (27) introduce a test to examine the spatial variability of each independent variable. Despite the advantages of GWR in terms of addressing the problem of spatial non-stationarity, the technique’s complexity in estimation and calibration makes researchers more hesitant to use it. ANOVA tests offer a way for comparison between OLS and GWR models (16) . In this paper, the above models are used in the context of optimal site selection. An application for the location of a mobility center is presented in the case study. The data used in this research was collected in the context of the project “Development and operation of a pilot mobility center in the Municipality of Kalamaria in the framework of the project MOBINET”. The project was implemented by the Hellenic Institute of Transport and it aimed at establishing a mobility center in the Municipality of Kalamaria (Thessaloniki, Greece, a map of which is shown in Figure 1). The objective of the mobility center is to provide services that will assist the mobility of the citizens in the greater urban area. The main services of the mobility center established in the Municipality of Kalamaria are: point-to-point mobility guidance and support; mobility guidance to predefined points of interest and the region’s gateways (port, airport, etc.); information provision about urban transport and points of interest; support for mobility impaired people; and ticketing services for urban and interurban transport. The mobility center is in operation since July 2008. A questionnaire survey was conducted prior to the mobility centre development in March 2008 aiming to acquire the mobility characteristics in the Municipality of Kalamaria, the needs and requirements of the citizens, the factors that affect the choice of the mode to be used in their trips, their preferences on the services of a mobility center, and other mobility oriented attributes. 600 adult citizens responded to the survey (driving is only allowed to people above 18 years of age in Greece, and excluding younger participants was essential in making the mode choice realistic). The questionnaire was disseminated to a random sample at the data-collection sites. The survey was organized in two parts. First, a series of questions pertaining to the travel choices of the respondents and the mobility center were asked. Then, demographic and socioeconomic questions were asked, including age, sex, marital status, number of kids, education level and occupation. An advantage of this ordering of the two parts of the survey, i.e., asking socioeconomic questions after the main part of the survey, is that the respondents are not made explicitly aware of their socioeconomic status when answering the questions, and as such response bias may be reduced. The questions included in the first part of the questionnaire address the following topics: • Transport mode(s) the respondents use in their daily trips • Frequency of the use of each transport mode • Indication of the three most important destinations • The degree that a list of factors affect the choice of mode the respondents use for their daily trips The degree that a list of factors discourage the respondents from using transit services • Level of effectiveness of several means of awareness (tables of journeys located in the stops, Internet, VMS, etc.) in support of the respondents’ mobility • Usefulness of a series of services of a mobility center • Frequency in the potential use of a mobility center In addition to the data collected in the above survey, additional information was collected for the needs of the present research. This information refers to bus routes, average vehicle speed in the study area, number of lanes in the road network, distances from key facilities (stadiums, parking stations, etc.). The first phase in the conducted research included the selection of the variables and their pre- processing. The most important parameters need to be identified and collected to the same reference level. In this research, these variables are the number of population, number of bus stations, number of bus routes, points of interest, total length of road network around the reference points, distance (logarithmic) from shopping center, distance (logarithmic) from stadium, distance (logarithmic) from parking charging schemes, average speed, average number of road lanes. The weights of the selected locations need to be allocated, with smoothing decay function, to street intersections. The logarithm of the main distance variables was considered as this captures the perceived differences in distances in a more natural way. This is a common transformation for entering distances in model specifications and reflects the non-linearity in the perception of distances. The process was performed using the following tools: ESRI ArcGIS 10, mostly for data processing and transformation, but mainly the R framework for statistical computing (32) , using the functionality included in the following packages: maptools, spgwr, spdep (33) for data analysis and ggplot2 (34) for visualization. In this research, the objective is demand-oriented. Facilities (kiosks/stations) are allocated in a way that the demand served is optimized. In this approach, the source of demand is within a distance or time of them (7) , so that the facility covers the demand. This type of demand has been used for public services, emergency and private sector. The data used in this research came from various sources and included layers of blocks with attributes of the population (e.g. average income), number of households, number of residential population, layer and attributes of bus stations with information about the bus routes, road network of the municipality of Kalamaria, with information about the road length, average speed, number of lanes, and points of interest (libraries, centers for citizens service etc.). The variables selected were then collected to the same reference level, the street intersections in this case, applying disaggregation and aggregation where needed. The data used are summarized in Table 1 and explained below. The columns of Table 1 refer to the four candidate locations for the mobility center, as per the original project. The data have been defined based on the street intersections in the study area. Whenever needed, the assumption of a coverage radius of 200 meters was made. This assumption is consistent with a reasonable walking distance for mobility services. The total number of residents who live 200 meters around the street intersections was added. Population provides a quantification of the service’s demand. Total number of points of interest that are 200 meters around the street intersections. The concentration of people around them in peak-hour is increased and it suggests a demand indicator. The total number of bus routes 200 meters around the street intersections. The correlation with the number of bus stations is approximately the same everywhere around the area, and indicates that every station is being used by about four bus routes. As a result, the number of stations was not added as separate variable in the ...
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... Moreover, the lower Akaike Information Criterion (AIC) indicates that their predicted values are closer to the observed ones. Furthermore, spatial econometrics models and GWR have been applied to model land-use changes (Wang et al. 2011), optimal location of facilities (Efthymiou et al. 2012) and annual average daily traffic (Zhao & Park 2004). Most of the contributions using those models assume that the dependent variable , house price or dwelling rent, is continuous. ...
Typically, urban development models have been based on aggregate principles. UrbanSim (Waddell et al. 2003) is among a new breed of models that use microsimulation in an effort to overcome the limitations of earlier models and provide a more dynamic and detailed paradigm. The advantages and disadvantages of using microsimulation are not within the scope of this chapter, but the main implication is that more data, as well as more detailed ones are required for microsimulation thanfor aggregate models.
In the context of the SustainCity project (http://www.sustaincity.org), three European cities (Brussels, Paris and Zürich, described in other chapters of this handbook) have been modelled using the land use microsimulation platform UrbanSim.
This platform relies on various models interacting with each other, to predict long–term urban development. The aim of this chapter is to provide some econometric insight into this process.
A common set of notations and assumptions are first defined, and the more common model structures (linear regression, multinomial logit, nested logit, mixed MNL and latent variable models) are described in a consistent way.
Special treatments and approaches that are required due to the specific nature of the data in this type of applications (i.e. involving very large number of alternatives, and often exhibiting endogeneity, correlation, and (pseudo–)panel data properties) are discussed. For example, importance sampling, spatial econometrics, Geographically Weighted Regression (GWR) and endogeneity issues are covered.
Specific examples of the following models: (i) household location choice model, (ii) jobs location/firmography, (iii) real estate price model, and (iv) land development model, are demonstrated in the context of the case studies in Brussels, Paris and Zürich. Finally, lessons learned in relation to the econometric models from these case studies are summarized.
... Moreover, how effectively vehicles are shuffled between stations strongly affects vehicle availability, which, in turn, impacts demand for the service. Simulation-based approaches have been used to infer the viability of various rebalancing schemes and, in turn, gauge consumer demand for one-way car-sharing, e.g.,1112131415. Initial findings suggest that one-way services are ideally suited for densely-populated urban centers. ...
The objective of this work is to provide analytical guidelines and financial justification for the design of shared-vehicle mobility-on-demand systems. Specifically, we consider the fundamental issue of determining the appropriate number of vehicles to field in the fleet, and estimate the financial benefits of several models of car sharing. As a case study, we consider replacing all modes of ion in a city such as Singapore with a fleet of shared automated vehicles, able to drive themselves, e.g., to move to a customer’s location. Using actual transportation data, our analysis suggests a shared-vehicle mobility solution can meet the personal mobility needs of the entire population with a fleet whose size is approximately 1/3 of the total number of passenger vehicles currently in operation.
... Moreover, how effectively vehicles are shuffled between stations strongly affects vehicle availability, which, in turn, impacts demand for the service. Simulation-based approaches have been used to infer the viability of various rebalancing schemes and, in turn, gauge consumer demand for one-way car-sharing, e.g.,1112131415. Initial findings suggest that one-way services are ideally suited for densely-populated urban centers. ...
The objective of this work is to provide analytical guidelines and financial justification for the design of shared-vehicle mobility-on-demand systems. Specifically, we consider the fundamental issue of determining the appropriate number of vehicles to field in the fleet, and estimate the financial benefits of several models of car sharing. As a case study, we consider replacing all modes of personal transportation in a city such as Singapore with a fleet of shared automated vehicles, able to drive themselves, e.g., to move to a customer’s location. Using actual transportation data, our analysis suggests a shared-vehicle mobility solution can meet the personal mobility needs of the entire population with a fleet whose size is approximately 1/3 of the total number of passenger vehicles currently in operation.
Road crash fatalities and debilitating injuries are preventable, both before and after the crash occurrence. The provision of medical care in cases of road crashes is among the most defining elements in trauma handling, with the care provided within the first hour (a period frequently termed as the golden hour) significantly decreasing mortality. As a result, the reduction in response times often represents one of the top priorities, especially for large-scale urban conglomerations. In this research, response times in cases of road crash emergencies in urban networks were investigated, particularly correlating important features that affect them, such as location accessibility, type of emergency or crash, and environmental conditions (in this case weather). Besides the response times, all other data came from opportunistic data sources, available for any region in the world. The methodological framework leverages advances from the field of spatial econometric modeling, which explicitly take spatial relationships into consideration. Incorporating the spatial dimension, in turn, may capture location-specific relationships, weather effects, or other significant elements and provide detailed results on response times for alternative cases of emergency. The application was performed over a suitable metropolis case, namely, the urban area of Riyadh, Saudi Arabia, while the results offer valuable insight that can be exploited for a meta-analysis aiming at improving the system's performance. Results suggest that besides accident severity and distance from the central business district (CBD) and points of interest, visibility and wind play a role in modeling emergency crew response times for the case of Riyadh.
