MODE CHOICE ANALYSIS: THE DATA, THE MODELS AND FUTURE AHEAD

By Minal, Ch. and Ravi Sekhar

Mode choice is one of the most vital stages in transportation planning process and it has direct impact on the policy making decisions. Mode choice models deals very closely with the human choice making behaviour and thus continues to attract researchers for further exploration of commuter’s choice making process. The objective of this study is to carryout detailed review on various modeling methods of mode choice analysis and bottlenecks associated with the same. The factors that affect the psyche of the travelers have been discussed; further various types of data required and their method of collection has been briefed up. This paper particularly emphasizes on statistical mode choice models such as multinomial logit and probit models as well as recent advanced soft computing techniques such as Artificial Neural Network models (ANN) and Fuzzy approach model that are employed for modal split analysis. Comparative analysis were made among various modeling techniques for modeling the complex mode choice of behaviour of models carried out by various researchers in the literature and a discussion on the need of future hybrid soft computing models has been attempted.

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more about urban travel behavior:

USING STRUCTURAL EQUATIONS MODELLING TO UNRAVEL THE INFLUENCE OF LAND USE PATTERNS ON TRAVEL BEHAVIOR OF URBAN ADULT WORKERS OF PUGET SOUND REGION

Feasibility of Voluntary Reduction of Private Car Use

What if you live in the wrong neighborhood? The impact of residential neighborhood type dissonance on distance traveled

Vehicle Miles Traveled and the Built Environment: Evidence from Vehicle Safety Inspection Data

Residential Self-Selection and Its Effects on Urban Commute Travels in Iranian Cities Compared to US, UK, and Germany

MODELING THE TRAVEL BEHAVIOR IMPACTS OF MICRO-SCALE LAND USE AND SOCIO-ECONOMIC FACTORS

Determinants of Automobile Use: A Comparison of Germany and the U.S

GIS MODELLING IN THEMATIC MAPPING OF LAND COVER CHANGES IN THE FOREST-STEPPE REGION OF RUSSIA

By ELIZAVETA KHAZIEVA

Nowadays there are many remote sensing methods and tools, which help to deeply understand the land cover processes on the large area without field researches. The cartographic modeling is one feasible way to analyze and deeply understand the data and processes which take place in the region. A combination of different data (such as remote sensing data, statistical information, historical maps and others), a knowledge of the territory ensures integral investigation, and a better demonstration of the result. There are many different approaches and models, one of them being thematic cartography. This is part of cartography focusing on natural phenomena, social, political and economic issues, combining visualization and exploration methods, and targeting and supporting different groups of users (Tikunov, 1997). Models are useful and used in a vastarray of GIS applications, from simple evaluation to the prediction of future landscapes. Cartographic modelling is a general methodology for the analysis and synthesis of geographical data. It employs what amount to an algebra in which single-factor maps are treated as variables that can be flexibly manipulated using an integrated set of functions (Paul et. al., 1991). The main trends of landscape changes is croplands decreasing especially in the 1990s, the situation beginning to improve by 2000 – 2006s. It probably has to do with the reforming procedure which had been started since the 1900s. Around 2000, the economic situation in Russia had stabilized again (Ioffe et al., 2008). For a better understanding of the impacts caused by political and economic developments on land use, further studies are necessary. The developed model has to be amended by adding some socio-economic data. It would help to better understand the process in a particular area and would allow to emphasize the drivers of changes more precisely.

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More about GIS applications:

Sustainable Urban Growth Management Using What-If?

Monitoring and modeling the urban growth of mid-size cities in Iran by Markov model: the case study of Zanjan City

Measurement and Monitoring of Urban Sprawl in a Rapidly Growing Region Using Entropy

URBAN ENVIRONMENTAL MANAGEMENT: MONITORING, GIS, AND MODELING

GIS-based urban modelling: practices, problems, and prospects

A GIS Approach to Evaluating Streetscape and Neighborhood Walkability

URBAN CHANGE MONITORING USING GIS AND REMOTE SENSING TOOLS IN KATHMANDU VALLEY (NEPAL)

An integrative GIS and remote sensing model for place-based urban vulnerability analysis

 

ECONOMIC DEVELOPMENT AND CHANGES IN CAR OWNERSHIP PATTERNS

By Anna Matas and Josep-LLuís Raymond

The contributions of this paper are twofold: On the one hand, the paper analyses the factors determining the growth in car ownership in Spain over the last two decades, and, on the other, the paper provides empirical evidence for a controversial methodological issue. From a methodological point of view, the paper compares the two alternative decision mechanisms used for modelling car ownership: ordered-response versus unordered-response mechanisms. A discrete choice model is estimated at three points in time: 1980, 1990 and 2000. The study concludes that on the basis of forecasting performance, the multinomial logit model and the ordered probit model are almost undistinguishable. As for the empirical results, it can be emphasised that income elasticity is not constant and declines as car ownership increases. Besides, households living in rural areas are less sensitive than those living in urban areas. Car ownership is also sensitive to the quality of public transport for those living in the largest cities. The results also confirmed the existence of a generation effect, which will vanish around the year 2020, a weak life-cycle effect, and a positive effect of employment on the number of cars per household. Finally, the change in the estimated coefficients over time reflects an increase in mobility needs and, consequently, an increase in car ownership.

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Potential Impacts of Climate Change on U.S. Transportation

UNDERSTANDING PERCEPTIONS OF ACCESSIBILITY AND MOBILITY THROUGH STRUCTURATION THEORY

How the Built Environment Influences Non-Work Travel: Theoretical and Empirical Essays 

Determinants of Automobile Use: A Comparison of Germany and the U.S.

Challenges of urban transport in developing countries- a summary

A dynamic formulation for car ownership modeling

By Cinzia Cirillo y, Renting Xu , and Fabian Bastinyz

Discrete choice models are commonly used in transportation planning and modeling, but their theoretical basis and applications have been mainly developed in a static context. In this paper, we propose an estimation technique for analyzing the impact of technological changes on the dynamic of consumer demand. The proposed research presents a dynamic formulation that explicitly models market evolution and accounts for consumers' expectations of future product characteristics. The timing of consumers' decisions is formulated as a regenerative optimal stopping problem where the agent must decide on the optimal time of purchase. This model frame will be further improved by modeling the choice from a set of di erentiated products whose characteristics randomly change over time. The framework proposed is developed and applied in the context of car ownership.

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mroe about urban transportation planning:

USING STRUCTURAL EQUATIONS MODELLING TO UNRAVEL THE INFLUENCE OF LAND USE PATTERNS ON TRAVEL BEHAVIOR OF URBAN ADULT WORKERS OF PUGET SOUND REGION

MATHEMATICAL MODELS OF TRANSPORTATION AND NETWORKS

Addressing Urban Transportation Equity in the United States

Transportation Policy for Poverty Reduction and Social Equity

A REGIONAL ANALYSIS OF URBAN POPULATION AND TRANSPORT ENERGY CONSUMPTION

Climate change and urban transportation systems

Vehicle Miles Traveled and the Built Environment: Evidence from Vehicle Safety Inspection Data

A THEORETICAL APPROACH TO CAPABILITIES OF THE TRADITIONAL URBAN FORM IN PROMOTING SUSTAINABLE TRANSPORTATION