scholarly journals Joint Residence-Workplace Location Choice Model Based on Household Decision Behavior

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Pengpeng Jiao ◽  
Tuo Sun ◽  
Jin Guo ◽  
Yangwei Li
Keyword(s):  
Choice Model ◽  
Mixed Logit ◽  
Choice Models ◽  
Urban Land Use ◽  
Beijing City ◽  
Household Decision ◽  
Decision Sequence ◽  
Land Use Types ◽  
Residence Location ◽  
Relevant Factors

Residence location and workplace are the two most important urban land-use types, and there exist strong interdependences between them. Existing researches often assume that one choice dimension is correlated to the other. Using the mixed logit framework, three groups of choice models are developed to illustrate such choice dependencies. First, for all households, this paper presents a basic methodology of the residence location and workplace choice without decision sequence based on the assumption that the two choice behaviors are independent of each other. Second, the paper clusters all households into two groups, choosing residence or workplace first, and formulates the residence location and workplace choice models under the constraint of decision sequence. Third, this paper combines the residence location and workplace together as the choice alternative and puts forward the joint choice model. A questionnaire survey is implemented in Beijing city to collect the data of 1994 households. Estimation results indicate that the joint choice model fits the data significantly better, and the elasticity effects analyses show that the joint choice model reflects the influences of relevant factors to the choice probability well and leads to the job-housing balance.

scholarly journals Integrated Model of Joint Residence-Workplace Location Choice and Commute Behavior Using Latent Class and Mixed Logit Methods

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Pengpeng Jiao ◽  
Meiqi Liu ◽  
Jin Guo
Keyword(s):  
Latent Class ◽  
Choice Model ◽  
Location Choice ◽  
Rapid Development ◽  
Latent Class Model ◽  
Mixed Logit ◽  
Integrated Model ◽  
Model Framework ◽  
Class Model ◽  
Residence Location

With the rapid development of urbanization and motorization, urban commute trips are becoming increasingly serious due to the unbalanced distribution of residence and workplace land-use types in most Chinese cities. To explore the inherent interrelations among residence location, workplace, and commute trip, an integrated model framework of joint residence-workplace location choice and commute behavior is put forward based on the personal trip survey data of Beijing in 2005. First, to extract households’ different choice characteristics, this paper presents a latent class model, clusters all households into several groups, and analyzes the conditional probability of each group. Second, the paper integrates the residence location and workplace together as the joint choice alternative, employs the socioeconomic factors, individual attributes, household attributes, and trip characteristics as explanatory variables, and formulates the joint residence-workplace location choice model using mixed logit method. Estimations of the latent class model show that four latent groups fit the data best. Further results of the joint residence-workplace location choice model indicate that there exist significantly different choice characteristics in each latent group. Generally, the integrated model framework outperforms traditional location choice methods.

scholarly journals Modeling enterprise location choice decision behavior

2021 ◽  
Vol 14 (1) ◽  
pp. 669-691
Author(s):  
Nguyen Cao Y
Keyword(s):  
Spatial Correlation ◽  
Discrete Choice ◽  
Choice Model ◽  
Location Choice ◽  
Mixed Logit ◽  
Multinomial Logit ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Spatial Effects ◽  
Modeling Framework

This study presents a location choice model that incorporates urban spatial effects for enterprises. A modeling framework is developed to analyze decisions regarding location choice for enterprises using a series of discrete choice models including multinomial logit without any urban spatial effects, multinomial logit incorporating urban spatial effects, and mixed logit incorporating urban spatial effects. In this framework, urban spatial effects, such as the urban spatial correlation among enterprises in deterministic terms and the urban spatial correlation among zones in the error term, are captured by mixed logit models in particular and discrete choice models in general. The results indicate that the urban spatial effects and the land prices in a given zone strongly affect the decision-making process of all the enterprises in the Tokyo metropolitan area. Moreover, the important role of urban spatial effects in the proposed model will be clarification through comparing the three above models. This comparison will be implemented on the basis of three types of indicators such as the log likelihood ratio, Akaike information indicator, and hit ratio of each model.

THE IMPACT OF DIFFERENT URBAN LAND USE TYPES ON AIR POLLUTION IN THE MEGACITY OF SÃO PAULO

2020 ◽  
Vol 7 (1) ◽  
pp. 91
Author(s):  
Júlio Barboza Chiquetto ◽  
Maria Elisa Siqueira Silva ◽  
Rita Yuri Ynoue ◽  
Flávia Noronha Dutra Ribieiro ◽  
Débora Souza Alvim ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Urban Land ◽  
Sao Paulo ◽  
Urban Land Use ◽  
São Paulo ◽  
Land Use Types ◽  
The Impact

A poluição do ar é influenciada por fatores naturais e antropogênicos. Quatro pontos de monitoramento (veicular, comercial, residencial e background urbano (BGU))da poluição do ar em São Paulo foram avaliados durante 16 anos, revelando diferenças significativas devidoao uso do solo em todas as escalas temporais. Na escala diurna, as concentrações de poluentes primários são duas vezes mais altas nos pontos veicular e residencial do que no ponto BGU, onde a concentração de ozonio (O3) é 50% mais alta. Na escala sazonal, as concentrações de monóxido de carbono(CO) variaram em 80% devido ao uso do solo, e 55% pela sazonalidade.As variações sazonais ede uso do solo exercem impactos similares nas concentrações de O3 e monóxido de nitrogênio (NO). Para o material particulado grosso (MP10) e o dióxido de nitrogênio(NO2), as variações sazonais são mais intensas do que as por uso do solo. Na série temporal de 16 anos, o ponto BGU apresentou correlações mais fortes e significativas entre a média mensal de ondas longas (ROL) e o O3 (0,48) e o MP10 (0,37), comparadas ao ponto veicular (0,33 e 0,22, respectivamente). Estes resultados confirmam que o uso do solo urbano tem um papel significativo na concentração de poluentes em todas as escalas de análise, embora a sua influência se torne menos pronunciada em escalas maiores, conforme a qualidade do ar transita de um sistema antropogênico para um sistema natural. Isto poderá auxiliar decisões sobre políticas públicas em megacidades envolvendo a modificação do uso do solo.

scholarly journals Micropollutants in Urban Stormwater Runoff of Different Land Uses

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1312
Author(s):  
Daniel Wicke ◽  
Andreas Matzinger ◽  
Hauke Sonnenberg ◽  
Nicolas Caradot ◽  
Rabea-Luisa Schubert ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Land Use ◽  
Surface Waters ◽  
Flame Retardants ◽  
Stormwater Runoff ◽  
Monitoring Program ◽  
Quality Standards ◽  
Urban Land Use ◽  
Organic Micropollutants ◽  
Land Use Types

The main aim of this study was a survey of micropollutants in stormwater runoff of Berlin (Germany) and its dependence on land-use types. In a one-year monitoring program, event mean concentrations were measured for a set of 106 parameters, including 85 organic micropollutants (e.g., flame retardants, phthalates, pesticides/biocides, polycyclic aromatic hydrocarbons (PAH)), heavy metals and standard parameters. Monitoring points were selected in five catchments of different urban land-use types, and at one urban river. We detected 77 of the 106 parameters at least once in stormwater runoff of the investigated catchment types. On average, stormwater runoff contained a mix of 24 µg L−1 organic micropollutants and 1.3 mg L−1 heavy metals. For organic micropollutants, concentrations were highest in all catchments for the plasticizer diisodecyl phthalate. Concentrations of all but five parameters showed significant differences among the five land-use types. While major roads were the dominant source of traffic-related substances such as PAH, each of the other land-use types showed the highest concentrations for some substances (e.g., flame retardants in commercial area, pesticides in catchment dominated by one family homes). Comparison with environmental quality standards (EQS) for surface waters shows that 13 micropollutants in stormwater runoff and 8 micropollutants in the receiving river exceeded German quality standards for receiving surface waters during storm events, highlighting the relevance of stormwater inputs for urban surface waters.

scholarly journals A Novel Query Strategy-Based Rank Batch-Mode Active Learning Method for High-Resolution Remote Sensing Image Classification

2021 ◽  
Vol 13 (11) ◽  
pp. 2234
Author(s):  
Xin Luo ◽  
Huaqiang Du ◽  
Guomo Zhou ◽  
Xuejian Li ◽  
Fangjie Mao ◽  
...  
Keyword(s):  
Land Use ◽  
Active Learning ◽  
Euclidean Distance ◽  
Urban Land Use ◽  
Misclassification Rate ◽  
Training Set ◽  
Batch Mode ◽  
Land Use Types ◽  
Information Divergence ◽  
Uncertainty Score

An informative training set is necessary for ensuring the robust performance of the classification of very-high-resolution remote sensing (VHRRS) images, but labeling work is often difficult, expensive, and time-consuming. This makes active learning (AL) an important part of an image analysis framework. AL aims to efficiently build a representative and efficient library of training samples that are most informative for the underlying classification task, thereby minimizing the cost of obtaining labeled data. Based on ranked batch-mode active learning (RBMAL), this paper proposes a novel combined query strategy of spectral information divergence lowest confidence uncertainty sampling (SIDLC), called RBSIDLC. The base classifier of random forest (RF) is initialized by using a small initial training set, and each unlabeled sample is analyzed to obtain the classification uncertainty score. A spectral information divergence (SID) function is then used to calculate the similarity score, and according to the final score, the unlabeled samples are ranked in descending lists. The most “valuable” samples are selected according to ranked lists and then labeled by the analyst/expert (also called the oracle). Finally, these samples are added to the training set, and the RF is retrained for the next iteration. The whole procedure is iteratively implemented until a stopping criterion is met. The results indicate that RBSIDLC achieves high-precision extraction of urban land use information based on VHRRS; the accuracy of extraction for each land-use type is greater than 90%, and the overall accuracy (OA) is greater than 96%. After the SID replaces the Euclidean distance in the RBMAL algorithm, the RBSIDLC method greatly reduces the misclassification rate among different land types. Therefore, the similarity function based on SID performs better than that based on the Euclidean distance. In addition, the OA of RF classification is greater than 90%, suggesting that it is feasible to use RF to estimate the uncertainty score. Compared with the three single query strategies of other AL methods, sample labeling with the SIDLC combined query strategy yields a lower cost and higher quality, thus effectively reducing the misclassification rate of different land use types. For example, compared with the Batch_Based_Entropy (BBE) algorithm, RBSIDLC improves the precision of barren land extraction by 37% and that of vegetation by 14%. The 25 characteristics of different land use types screened by RF cross-validation (RFCV) combined with the permutation method exhibit an excellent separation degree, and the results provide the basis for VHRRS information extraction in urban land use settings based on RBSIDLC.

scholarly journals A Trip Purpose-Based Data-Driven Alighting Station Choice Model Using Transit Smart Card Data

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Kai Lu ◽  
Alireza Khani ◽  
Baoming Han
Keyword(s):  
Open Systems ◽  
Choice Model ◽  
Mixed Logit ◽  
Random Coefficients ◽  
Logit Models ◽  
Transit Systems ◽  
Trip Purpose ◽  
Mixed Logit Models ◽  
Key Factor ◽  
Rich Data

Automatic fare collection (AFC) systems have been widely used all around the world which record rich data resources for researchers mining the passenger behavior and operation estimation. However, most transit systems are open systems for which only boarding information is recorded but the alighting information is missing. Because of the lack of trip information, validation of utility functions for passenger choices is difficult. To fill the research gaps, this study uses the AFC data from Beijing metro, which is a closed system and records both boarding information and alighting information. To estimate a more reasonable utility function for choice modeling, the study uses the trip chaining method to infer the actual destination of the trip. Based on the land use and passenger flow pattern, applying k-means clustering method, stations are classified into 7 categories. A trip purpose labelling process was proposed considering the station category, trip time, trip sequence, and alighting station frequency during five weekdays. We apply multinomial logit models as well as mixed logit models with independent and correlated normally distributed random coefficients to infer passengers’ preferences for ticket fare, walking time, and in-vehicle time towards their alighting station choice based on different trip purposes. The results find that time is a combined key factor while the ticket price based on distance is not significant. The estimated alighting stations are validated with real choices from a separate sample to illustrate the accuracy of the station choice models.

scholarly journals Mode choice analysis using discrete choice model from transport user (Case study: Jakarta LRT, Indonesia)

2018 ◽  
Vol 181 ◽  
pp. 03001
Author(s):  
Dwi Novi Wulansari ◽  
Milla Dwi Astari
Keyword(s):  
Discrete Choice ◽  
Public Transportation ◽  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Light Rail ◽  
Choice Analysis ◽  
Mode Choice Model

Jakarta Light Rail Transit (Jakarta LRT) has been planned to be built as one of mass rail-based public transportation system in DKI Jakarta. The objective of this paper is to obtain a mode choice models that can explain the probability of choosing Jakarta LRT, and to estimate the sensitivity of mode choice if the attribute changes. Analysis of the research conducted by using discrete choice models approach to the behavior of individuals. Choice modes were observed between 1) Jakarta LRT and TransJakarta Bus, 2) Jakarta LRT and KRL-Commuter Jabodetabek. Mode choice model used is the Binomial Logit Model. The research data obtained through Stated Preference (SP) techniques. The model using the attribute influences such as tariff, travel time, headway and walking time. The models obtained are reliable and validated. Based on the results of the analysis shows that the most sensitive attributes affect the mode choice model is the tariff.

Modeling Mode Choice of Air Passengers’ Ground Access to Brisbane Airport

2020 ◽  
Vol 2674 (11) ◽  
pp. 756-767
Author(s):  
Md Mosabbir Pasha ◽  
Mark D. Hickman ◽  
Carlo G. Prato
Keyword(s):  
Online Survey ◽  
Travel Demand ◽  
Choice Model ◽  
Mode Choice ◽  
Mixed Logit ◽  
Data Access ◽  
Developed Countries ◽  
Stated Preferences ◽  
Value Of Time ◽  
Access Mode

Like other developed countries, Australia is experiencing considerable growth in air travel demand. This growth is putting tremendous pressure on airports to improve the capacity and level of service of access and egress modes. The main goal of this study was to develop a robust mode choice model for passengers’ access to airports, in the context of Brisbane, Australia. The novelty of this study includes (1) the use of recent techniques to combine passengers’ revealed and stated preferences for mode of access to airports and (2) the development of both traditional multinomial logit (MNL) as well as mixed logit (MXL) models using these data. The data were collected from 1,435 passengers through an online survey of both revealed and stated preferences, with the stated preferences pivoting on their most recent trip to Brisbane Airport. With these data, access mode choice models were estimated. MNL and MXL models were estimated that directly considered the effects of passenger mode choice inertia as well as novel passenger-specific characteristics. These factors had a large, statistically significant effect on the estimated models. The benefit of the MXL model was shown in the results, as the passengers’ value of time from the MXL model was A$105.15/h, which was strikingly lower than the MNL-estimated value of time. In addition, there were notably high direct elasticities for bus and shuttle with respect to travel time, and for car and taxi with respect to travel cost.

Effect of Route Choice Models on Estimating Network Capacity Reliability

2000 ◽  
Vol 1733 (1) ◽  
pp. 63-70 ◽  
Author(s):  
Anthony Chen ◽  
Maya Tatineni ◽  
Der-Horng Lee ◽  
Hai Yang
Keyword(s):  
Network Performance ◽  
Choice Behavior ◽  
Choice Model ◽  
Route Choice ◽  
Network Capacity ◽  
Service Level ◽  
Choice Models ◽  
Transportation Systems ◽  
Traffic Demand ◽  
Route Choice Behavior

The issue of planning for adequate capacity in transportation systems to accommodate growing traffic demand is becoming a serious problem. Recent research has introduced "capacity reliability" as a new network performance index. Capacity reliability is defined as the probability that a network can accommodate a certain volume of traffic demand at a required service level given variable arc capacities, while accounting for drivers' route choice behavior. Previous papers on this topic provide a comprehensive methodology for assessing capacity reliability along with extensive simulation results. However, an important issue that remains is what type of route choice model should be used to model driver behavior in estimating network capacity reliability. Three different route choice models (one deterministic and two stochastic models) are compared, and the effect of using each of these models on estimating network capacity reliability is examined.

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