Contributions to the calibration of integrated land use and transportation models

International audienceThe need for land use and transport integrated modelling (LUTI modelling) as a decision aid tool in urban planning, has become apparent. Instantiating such models on cities, requires a substantial data collection, model structur-ing and parameter estimation effort. This work is a partial effort towards the integrated calibration of LUTI models. It considers one of the most widely used LUTI models and softwares, Tranus. The usual calibration approach for Tranus is briefly reviewed, then the calibration of Tranus' land use module is reformulated as an optimisation problem, proposing a clear basis for future fully integrated calibration. We analyse the case of transportable and non-transportable economic sectors. We also discuss how to validate calibration results and propose to use synthetic data generated from real world problems in order to assess convergence properties and accuracy of calibration methods. Finally, results of this methodology are presented for real world scenarios

Co-Evolution of Transportation and Land Use: Modeling HIstorical Dependencies in Land Use and Decision-Making

The interaction between land use and transportation has long been the central issue in urban and regional planning. Models of such interactions provide vital information to support many public policy decisions, such as land supply, infrastructure provision, and growth management. Both the transportation and land use systems exhibit historical dependencies in policy decisions. For instance, the expansion of a roadway today will change travel demand patterns, and make certain other roads more or less likely to be expanded in the future. A specific land supply decision made at one point in time, by changing the relative attractiveness of other areas in the region, can have a profound impact on future land supply decisions. Today’s land use decisions clearly influence future transportation policies and vice versa. This project examines the land use-transportation interaction from an evolutionary perspective — once a certain set of goals are determined and pursued by politicians and planners, their land supply and transportation investment decisions are, to a large extent, driven by their previous decisions and supply-demand dynamics in the urban system. Built upon this recognition of historical dependency and a transportation network growth model previously developed by the P.I., a model of the co-evolution of land use and transportation is proposed in this project. Different from existing integrated land use and transportation models that assume exogenous network investment decisions, the co-evolution model considers both land use growth and transportation network growth as endogenous and market-driven. The central research question is how market and policies translate into transportation facilities and land use developments on the ground. The co-evolution model achieves an Urban Growth Equilibrium, which is a useful concept for planning and policy analysis. An agent-based simulation approach is employed to integrate an existing land use model and the transportation network growth model

Potential Economic Consequences of Local Nonconformity to Regional Land Use and Transportation Plans Using a Spatial Economic Model

To achieve the greenhouse gas (GHG) reduction targets that are required by California’s global warming legislation (AB32), the state of California has determined that recent growth trends in vehicle miles traveled (VMT) must be curtailed. In recognition of this, Senate Bill 375 (SB375) requires regional governments to develop land use and transportation plans or Sustainable Community Strategies (SCSs) that will achieve regional GHG targets largely though reduced VMT. Although the bill requires such a plan, it does not require local governments to adopt general plans that conform to this plan. In California, it is local, not regional, governments that have authority over land development decisions. Instead, SB375 relies on democratic participatory processes and relatively modest financial and regulatory incentives for SCS implementation. As a result, it is quite possible that some local governments within a region may decide not to conform to their SCS. In this study, a spatial economic model (PECAS) is applied in the Sacramento region (California, U.S.) to understand what the economic and equity consequences might be to jurisdictions that do and do not implement SCS land use plans in a region. An understanding of these consequences provides insight into jurisdictions’ motivations for compliance and thus, strategies for more effective implementation of SB375

Development of a Multi-modal Travel Demand Module for the Regional Strategic Planning Model

Part of the Student Presentations from TRB Integrated land use and transportation models have evolved along a spectrum with simplistic sketch planning models on one end and sophisticated microsimulation models on the other. While each type of these models has its niche, they are largely unable to balance the flexibility and realism of microsimulation and the speed and interactiveness of simple models. The Regional Strategic Planning Model (RSPM) aims to fill this gap by taking a microsimulation approach but making other simplifications, to model first-order long-term outcomes of land use and transportation quickly. It takes into consideration the underlying uncertainties of long-term modeling by accepting a broad range of policy inputs and technology assumptions while allowing rapid simulations of hundreds of scenarios. The RSPM is one of a few operational modeling packages (along with EERPAT and RPAT) that have evolved from GreenSTEP, a microsimulation modeling package for state-level evaluation of strategies for reducing transportation energy consumption and greenhouse gas (GHG) emissions. Several ongoing projects are aiming to develop a common software framework for the family of strategic modeling tools and improve the policy sensitivity of multi-modal travel. In this study, we introduce the RSPM framework, and then primarily focus on the new development of a multi-modal travel demand module that links various policy inputs to households’ multi-modal travel and further to aggregate transportation outcomes (e.g. GHG emissions, traffic fatalities). We discuss our choice of model structures and specifications and then estimate the models utilizing a unique US nationwide dataset combining the 2009 US National Household Travel Survey (NHTS), EPA’s Smart Location Database, and the National Transit Database. This comprehensive dataset provides a rich set of variables capturing household social-demographics, multi-modal travel, built environment, and transportation supply. We conclude the paper with the results of validation and sensitivity tests, and a discussion of future work.https://pdxscholar.library.pdx.edu/trec_seminar/1135/thumbnail.jp

Unveiling E-bike potential for commuting trips from GPS traces

Common goals of sustainable mobility approaches are to reduce the need for travel, to facilitate modal shifts, to decrease trip distances and to improve energy efficiency in the transportation systems. Among these issues, modal shift plays an important role for the adoption of vehicles with fewer or zero emissions. Nowadays, the electric bike (e-bike) is becoming a valid alternative to cars in urban areas. However, to promote modal shift, a better understanding of the mobility behaviour of e-bike users is required. In this paper, we investigate the mobility habits of e-bikers using GPS data collected in Belgium from 2014 to 2015. By analysing more than 10,000 trips, we provide insights about e-bike trip features such as: distance, duration and speed. In addition, we offer a deep look into which routes are preferred by bike owners in terms of their physical characteristics and how weather influences e-bike usage. Results show that trips with higher travel distances are performed during working days and are correlated with higher average speeds. Usage patterns extracted from our data set also indicate that e-bikes are preferred for commuting (home-work) and business (work related) trips rather than for recreational trips

An integrated urban systems model with GIS

The purpose of the research is to develop an integrated urban systems model, which will assist in formulating a better land use-transportation policy by simulating the relationships between land use patterns and travel behavior, integrated with geographic information systems (GISs). In order to make an integrated land use-transportation model possible with the assistance of GISs technologies, the following four sub-systems have been developed: (1) an effective traffic analysis zone generation system; (2) an iterative land use and transportation modeling system; (3) efficient interfaces between GIS and land use, and GIS and transportation models; and (4) a user-friendly graphic user interface (GUI) system. By integrating these sub-systems, a variety of alternative land use-transportation policies can be evaluated through the modification of input parameters in each simulation. Eventually, the developed model using a GIS will assist in formulating an effective land use policy by obtaining robust simulation results for both land use-transportation planners and decision makers. The model has been applied to the Urbana-Champaign area as well as to the Seoul region in Korea for a demonstration of the workings of the model.

Models of Transportation and Land Use Change: A Guide to the Territory

Modern urban regions are highly complex entities. Despite the difficulty of modeling every relevant aspect of an urban region, researchers have produced a rich variety models dealing with inter-related processes of urban change. The most popular types of models have been those dealing with the relationship between transportation network growth and changes in land use and the location of economic activity, embodied in the concept of accessibility. This paper reviews some of the more common frameworks for modeling transportation and land use change, illustrating each with some examples of operational models that have been applied to real-world settings.Transport, land use, models, review network growth, induced demand, induced supply

Multilevel (ML-ICLV) & Single Level Integrated Discrete Choice and Latent Variable (ICLV) Models Using Alternative Latent Structures' Conceptualizations

The aim of the present endeavor is to experiment on integrating discrete choice with latent variable (ICVL) models using alternative factorial structures’ conceptualizations and do so at both Single Level (Level 0) and Multilevel (ML-ICVL). In doing, specific independent variables amenable to alternative latent variables’ conceptualization were selected. These included: a) 1st-order latent variables (1st-order factors) (FM; FW), b) 1st-order latent variables (1st-order factors) (FM; FW) forming a 2nd-order factor (F), c) Multi-level (two-level) factorial structures (FML0; FML1 and FWL0; FWL1), and d) Bi-Factor factorial structures (FM; FW; FG). The results may be of use to researchers interested in using valid, reliable, and accurate structures of latent variables in ICLV models. We confirm that alternative latent structures of divergent factorial nature exist for the same observed variables, and may have different impact upon the dependent observed choice variable in the ICLV models. Second, DCE utility is conceptualized and estimated at both Level 0 and Level 1 and the differences are evident

Multilevel (ML-ICLV) & Single Level Integrated Discrete Choice and Latent Variable (ICLV) Models Using Alternative Latent Structures' Conceptualizations

The aim of the present endeavor is to experiment on integrating discrete choice with latent variable (ICVL) models using alternative factorial structures’ conceptualizations and do so at both Single Level (Level 0) and Multilevel (ML-ICVL). In doing, specific independent variables amenable to alternative latent variables’ conceptualization were selected. These included: a) 1st-order latent variables (1st-order factors) (FM; FW), b) 1st-order latent variables (1st-order factors) (FM; FW) forming a 2nd-order factor (F), c) Multi-level (two-level) factorial structures (FML0; FML1 and FWL0; FWL1), and d) Bi-Factor factorial structures (FM; FW; FG). The results may be of use to researchers interested in using valid, reliable, and accurate structures of latent variables in ICLV models. We confirm that alternative latent structures of divergent factorial nature exist for the same observed variables, and may have different impact upon the dependent observed choice variable in the ICLV models. Second, DCE utility is conceptualized and estimated at both Level 0 and Level 1 and the differences are evident