Evaluating the Effects of Transportation—Land-Use Policies on Housing Values and Household Welfare

1980 ◽  
Vol 12 (7) ◽  
pp. 747-764 ◽  
Author(s):  
A Anas
Keyword(s):  
Simulation Model ◽  
Discrete Choice ◽  
Transportation Planning ◽  
Large Scale ◽  
Travel Demand ◽  
Choice Theory ◽  
Property Values ◽  
Computationally Efficient ◽  
Demand Prediction ◽  
The Impact

In a previous article published in this journal (Anas, 1979a), a simulation model developed by the author was used to examine the impact of transit investment on property values in an urban transportation corridor that had a completely centralized employment distribution. The present paper examines the effect of rail-transit investment in the context of various scenarios which deal with urban employment decentralization, housing distribution, transportation pricing, and income composition. From these simulations it appears that under a variety of assumptions regarding urban change the taxation of short-run differential changes in property values caused by transit investment can raise only a small portion of the cost of typical transit investments. The distinctive feature of the simulation model is that it is consistent with the discrete-choice theory of travel demand currently used in transportation planning and travel-demand prediction. But whereas the state of the art in transportation planning ignores the simultaneity of transportation changes and price changes in the housing market, the model developed here is a first attempt to deal with these effects by incorporating discrete-choice theory into a Walrasian market-equilibration procedure. In addition to being a theoretical alternative to the classical bid-rent model, still made use of by urban economists, the new approach is computationally efficient and suitable for large-scale simulation.

scholarly journals The impact of secondary ice production on Arctic stratocumulus

2020 ◽  
Vol 20 (3) ◽  
pp. 1301-1316
Author(s):  
Georgia Sotiropoulou ◽  
Sylvia Sullivan ◽  
Julien Savre ◽  
Gary Lloyd ◽  
Thomas Lachlan-Cope ◽  
...  
Keyword(s):  
Large Scale ◽  
Cloud Condensation Nuclei ◽  
The Arctic ◽  
Computationally Efficient ◽  
Eddy Simulation ◽  
Scale Models ◽  
Break Up ◽  
Ice Production ◽  
Low Sensitivity ◽  
The Impact

Abstract. In situ measurements of Arctic clouds frequently show that ice crystal number concentrations (ICNCs) are much higher than the number of available ice-nucleating particles (INPs), suggesting that secondary ice production (SIP) may be active. Here we use a Lagrangian parcel model (LPM) and a large-eddy simulation (LES) to investigate the impact of three SIP mechanisms (rime splintering, break-up from ice–ice collisions and drop shattering) on a summer Arctic stratocumulus case observed during the Aerosol-Cloud Coupling And Climate Interactions in the Arctic (ACCACIA) campaign. Primary ice alone cannot explain the observed ICNCs, and drop shattering is ineffective in the examined conditions. Only the combination of both rime splintering (RS) and collisional break-up (BR) can explain the observed ICNCs, since both of these mechanisms are weak when activated alone. In contrast to RS, BR is currently not represented in large-scale models; however our results indicate that this may also be a critical ice-multiplication mechanism. In general, low sensitivity of the ICNCs to the assumed INP, to the cloud condensation nuclei (CCN) conditions and also to the choice of BR parameterization is found. Finally, we show that a simplified treatment of SIP, using a LPM constrained by a LES and/or observations, provides a realistic yet computationally efficient way to study SIP effects on clouds. This method can eventually serve as a way to parameterize SIP processes in large-scale models.

scholarly journals Economic and Mathematical Modeling of the Direct Effects of Large-Scale Infrastructure Projects on Developing Territories

2019 ◽  
pp. 78-89
Author(s):  
Olga Patrakeeva
Keyword(s):  
Economic Growth ◽  
Simulation Model ◽  
Large Scale ◽  
System Analysis ◽  
Distribution Functions ◽  
Management Policy ◽  
Infrastructure Projects ◽  
Railway Transport ◽  
Krasnodar Krai ◽  
The Impact

The problem of assessing the effects of infrastructure projects for territories is debatable. Modeling experience has been accumulated today, and elaborated macroeconomic models allow to identify causal relationships between the indicators of transport development and economic growth. The goal of this article is to define a simulation model of assessing the impact of transport projects on the economic growth of Krasnodar Krai exemplified by the Crimean Bridge project. The solution of this scientific problem requires taking into account different factors and complicated interrelationships within the framework of the regional social and economic system under consideration, using methods of system analysis and tools of economic and mathematical simulation. The simulation model reflects the scenario parameters of the capital management policy, highway transport freight turnover, highway transport freight turnover directly connected with the construction of Kerch Straight Bridge, carriage of goods by railway transport, carriage of goods by railway transport directly connected with the construction of Kerch Straight Bridge. The interrelations of this model’s parameters are established by the econometrics methods. In accordance with the produced scenarios the expected median values of the additional increment of the Krasnodar Krai GRP due to the increment of transportation associated with the Crimean Bridge operation are in the range between 0.97 % and 1.1 %. The most conservative scenario presumes the median value of 0.97 % and lower limit of 0.8 %. This tool can be used to assess the direct effect of railway and road construction for other Russian regions. The proposed simulation model will be further expanded by including further distribution functions of scenario variables and additional structural relationships.

Impact of Major Road Supply on Individual Travel Time Expenditure: An Exploration with a 30-Year Variation of Infrastructure and Travel

2018 ◽  
Vol 2672 (3) ◽  
pp. 56-68
Author(s):  
Ryosuke Abe ◽  
Kay W. Axhausen
Keyword(s):  
Travel Time ◽  
Large Scale ◽  
Travel Demand ◽  
Road Traffic ◽  
Land Development ◽  
Transportation Infrastructure ◽  
Major Road ◽  
Birth Cohorts ◽  
Data Set ◽  
The Impact

This study estimates the impact of major road supply on individual travel time expenditures (TTEs) using data that cover 30-year variations in transportation infrastructure and travel behavior. The impacts of the supply of road and rail infrastructure are estimated with a data set that combines records of large-scale household travel surveys in the Tokyo metropolitan area conducted in 1978, 1988, 1998, and 2008. Linear and Tobit models of individual TTEs are estimated by following the behavior of birth cohorts over the 30-year period. The models incorporate the changes in transportation infrastructure, measured as lane kilometers of two levels of major road stock and vehicle kilometers of urban rail service. The results show significant negative effects of lane kilometers for higher-level and lower-level major roads on the TTEs for all travel purposes and for commuting, after controlling for socioeconomic backgrounds and generations of individuals. This study discusses that, in Tokyo, the estimated effect is more likely to reflect the effect of a major road network per se on individual TTEs than the (indirect) effect of major road supply on individual TTEs working through land development activities (i.e., induced car travel demand). For example, the caveat is that actual road investment decisions still need to consider the induced component of road traffic in addition to the (direct) effect that is estimated in this study.

The effect of upzoning on house prices and redevelopment premiums in Auckland, New Zealand

Urban Studies ◽  
2020 ◽  
pp. 004209802094060
Author(s):  
Ryan Greenaway-McGrevy ◽  
Gail Pacheco ◽  
Kade Sorensen
Keyword(s):  
Land Use ◽  
New Zealand ◽  
Large Scale ◽  
House Prices ◽  
Property Values ◽  
Economic Potential ◽  
Land Use Regulations ◽  
Short Run ◽  
The Impact

We study the short-run effects of a large-scale upzoning on house prices and redevelopment premiums in Auckland, New Zealand. Upzoning significantly increases the redevelopment premium but the overall effect on house prices depends on the economic potential for site redevelopment, with underdeveloped properties appreciating relative to intensively developed properties. Notably, intensively developed properties decrease in value relative to similar dwellings that were not upzoned, showing that the large-scale upzoning had an immediate depreciative effect on pre-existing intensive housing. Our results show that the economic potential for site redevelopment is fundamental to understanding the impact of changes in land use regulations on property values.

scholarly journals SMASH (Simulation Model of Activity Scheduling Heuristics): Some Simulations

1996 ◽  
Vol 1551 (1) ◽  
pp. 88-94 ◽  
Author(s):  
Dick Ettema ◽  
Aloys Borgers ◽  
Harry Timmermans
Keyword(s):  
Decision Making ◽  
Simulation Model ◽  
Discrete Choice ◽  
Travel Demand ◽  
Process Models ◽  
Face Validity ◽  
Activity Scheduling ◽  
Activity Schedules ◽  
Travel Demand Management ◽  
Starting Point

Travel decision making is increasingly regarded as a highly complex process in which individuals not only decide about frequency of trips, travel modes, and routes, but also about activity participation and sequencing and timing and duration of activities and trips. This raises the question of whether or not traditional discrete-choice models still provide the best starting point for realistically modeling such a process. Some scholars consider computational process models (CPMs) a promising approach because they allow for heuristic search and suboptimal reasoning processes, which are typical for complex decision making. A model of activity scheduling, SMASH (Simulation Model of Activity Scheduling Heuristics), which incorporates aspects of discrete-choice modeling and CPMs, has been proposed. The model describes the pretrip planning phase, in which individuals decide which activities to perform, at what locations, at what times, in which sequence, and how to travel to the various activity sites. The calibration of this model, using data collected with the interactive computerized procedure MAGIC, has been described in the literature. The results indicated that when scheduling their activities, subjects seem to trade off attributes of activities (time constraints, duration), attributes of the schedule (time spent on activities, overall travel time, realism) and characteristics of the scheduling process (amount of effort already involved in the scheduling process) to obtain feasible schedules. More extensive tests, using simulation experiments, of the model's internal, predictive, and face validity are described. SMASH was used to predict subjects' activity schedules based on their activity agenda and information about their spatio-temporal circumstances. The predicted schedules were then compared with the activity schedules conceived by the subjects themselves under different circumstances, to assess the model's validity. The tests indicated that the model provided satisfactory results with respect to the reproduction of the observed activity schedules. The results of the validity test warrant the use of the model for assessing the effects of various policy measures such as time policies, land use policies, and travel demand management.

Simulation on the Behavior of Individual Vehicles with Influence Evaluation of Flow Rate and Speed Limits on Freeway

2014 ◽  
Vol 1022 ◽  
pp. 83-86
Author(s):  
Shu Yue Wu ◽  
Xiao Tong Yu ◽  
Zhong Wei He ◽  
Xin Wen
Keyword(s):  
Simulation Model ◽  
Traffic Flow ◽  
Flow Rate ◽  
Transportation Planning ◽  
Traffic Simulation ◽  
Speed Limits ◽  
Micro Simulation ◽  
The Impact ◽  
Scientific Tool

Traffic simulation, a powerful scientific tool, can be applied to both transportation planning and to transportation design and operations. In this paper, a micro-simulation model is developed to simulate the behavior of individual vehicles on the freeway and is used to evaluate the impact of changes in efficiency and safety resulting from changes to traffic flow and speed limits. All aforementioned influences are expressed at a quantitive level.

Study on the Regularity of EVs' Charging and Discharging Group Behaviors

2013 ◽  
Vol 860-863 ◽  
pp. 1173-1180
Author(s):  
Yu Zhang ◽  
Chen Fang ◽  
Jian Lin Yang ◽  
Shao Peng Liu
Keyword(s):  
Simulation Model ◽  
Electric Vehicles ◽  
Large Scale ◽  
Specific Behavior ◽  
Charging Time ◽  
Economic Operation ◽  
Charging Mode ◽  
The Impact ◽  
At Home

The large scale application of EVs (electric vehicles) is an inevitable trend at home and abroad. When lots of EVs are plugged into the grid, the impact of their charging load on the grid is not negligible; while the development of V2G technology allows EVs to discharge to the grid, it provides an opportunity for improving the safety and economic operation of the grid. The paper models the specific behavior features of the bus and private EVs from the mileage, charging mode and charging time three aspects. MATLAB is used for programming, and EVs sample are randomly selected. The EVs charging and discharging behavior probability is analyzed to determine the simulation model. Last, by the practical example, the impact of EVs charging and discharging behavior on the grid is calculated, and conclusions that match the reality can be drawn.

scholarly journals Coordinating Measurements in Uncertain Participatory Sensing Settings

2018 ◽  
Vol 61 ◽  
pp. 433-474 ◽  
Author(s):  
Alexandros Zenonos ◽  
Sebastian Stein ◽  
Nicholas R. Jennings
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Low Cost ◽  
Cost Effective ◽  
Environmental Data ◽  
Participatory Sensing ◽  
Sufficient Information ◽  
Mobility Patterns ◽  
Computationally Efficient ◽  
The Impact

Environmental monitoring allows authorities to understand the impact of potentially harmful phenomena, such as air pollution, excessive noise, and radiation. Recently, there has been considerable interest in participatory sensing as a paradigm for such large-scale data collection because it is cost-effective and able to capture more fine-grained data than traditional approaches that use stationary sensors scattered in cities. In this approach, ordinary citizens (non-expert contributors) collect environmental data using low-cost mobile devices. However, these participants are generally self-interested actors that have their own goals and make local decisions about when and where to take measurements. This can lead to highly inefficient outcomes, where observations are either taken redundantly or do not provide sufficient information about key areas of interest. To address these challenges, it is necessary to guide and to coordinate participants, so they take measurements when it is most informative. To this end, we develop a computationally-efficient coordination algorithm (adaptive Best-Match) that suggests to users when and where to take measurements. Our algorithm exploits probabilistic knowledge of human mobility patterns, but explicitly considers the uncertainty of these patterns and the potential unwillingness of people to take measurements when requested to do so. In particular, our algorithm uses a local search technique, clustering and random simulations to map participants to measurements that need to be taken in space and time. We empirically evaluate our algorithm on a real-world human mobility and air quality dataset and show that it outperforms the current state of the art by up to 24% in terms of utility gained.

scholarly journals Joint Attention Neural Model for Demand Prediction in Online Marketplaces

2020 ◽  
Vol 1 ◽  
pp. 6
Author(s):  
Ashish Gupta ◽  
Rishabh Mehrotra
Keyword(s):  
Inventory Management ◽  
Large Scale ◽  
Contextual Information ◽  
Neural Model ◽  
Superior Performance ◽  
Real World Data ◽  
Demand Prediction ◽  
Online Marketplaces ◽  
Textual Content ◽  
The Impact

As an increasing number of consumers rely on online marketplaces to purchase goods from, demand prediction becomes an important problem for suppliers to inform their pricing and inventory management decisions. Business volatility and the complexity of factors influence demand, which makes it a harder quantity to predict. In this paper, we consider the case of an online classified marketplace and propose a joint multi-modal neural model for demand prediction. The proposed neural model incorporates a number of factors including product description information (title, description, images), contextual information (geography, similar products) and historic interest to predict demand. Large-scale experiments on real-world data demonstrate superior performance over established baselines. Our experiments highlight the importance of considering, quantifying and leveraging the textual content of products and image quality for enhanced demand prediction. Finally, we quantify the impact of the different factors in predicting demand.

The Impact of Transit Investment on Housing Values: A Simulation Experiment

1979 ◽  
Vol 11 (3) ◽  
pp. 239-255 ◽  
Author(s):  
A Anas
Keyword(s):  
Logit Model ◽  
Travel Demand ◽  
Central City ◽  
Property Values ◽  
Location Decisions ◽  
Housing Values ◽  
Value Capture ◽  
Clean Break ◽  
Value Changes ◽  
The Impact

This paper uses a joint-choice logit model of travel demand and residential location to simulate the impact of urban rapid-transit investment on housing values within a radial corridor. The model developed is a clean break with the traditional urban economic theory. Instead the heterogeneous nature of travel and location decisions is recognized and the logit model, consistent with stochastic utility maximization, is employed. Simulation experiments reveal that the aggregate increase in property values caused by transit's impact on work trips is highly sensitive to the aggregate number of vacancies within the corridor. Under reasonable assumptions, transit investment tends to lower central-city property values, to increase central-city vacancies, and to raise suburban property values. It tends to help the poor move further away from the center and penetrate the inner suburbs. Depending on several influences, aggregate property values can increase or decrease and the change can often be statistically insignificant. Calculations show that an equitable taxation (and compensation) of property-value changes may raise a small to modest proportion of a transit system's construction cost. Several considerations suggest that even these modest estimates might be optimistic. These results help develop an improved perspective on ‘value-capture policy’ which has not, up to now, benefited from quantitative analysis. Major extensions of the model are briefly considered.

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