Assessing the impact of the built environment on travel behavior: a case study of Buffalo, New York

2011 ◽  
Vol 38 (4) ◽  
pp. 663-678 ◽  
Author(s):  
Andrew J. Tracy ◽  
Peng Su ◽  
Adel W. Sadek ◽  
Qian Wang
Keyword(s):  
New York ◽  
Built Environment ◽  
Travel Behavior ◽  
The Impact

The Impact of Zoning and Built Environment Characteristics on Transit, Biking, and Walking

2019 ◽  
Vol 11 (1) ◽  
pp. 108-129
Author(s):  
Andrew G. Mueller ◽  
Daniel J. Trujillo
Keyword(s):  
Land Use ◽  
Built Environment ◽  
Discrete Choice ◽  
Travel Behavior ◽  
Choice Model ◽  
Mode Choice ◽  
Land Use Regulation ◽  
Policy Action ◽  
Development Goals ◽  
The Impact

This study furthers existing research on the link between the built environment and travel behavior, particularly mode choice (auto, transit, biking, walking). While researchers have studied built environment characteristics and their impact on mode choice, none have attempted to measure the impact of zoning on travel behavior. By testing the impact of land use regulation in the form of zoning restrictions on travel behavior, this study expands the literature by incorporating an additional variable that can be changed through public policy action and may help cities promote sustainable real estate development goals. Using a unique, high-resolution travel survey dataset from Denver, Colorado, we develop a multinomial discrete choice model that addresses unobserved travel preferences by incorporating sociodemographic, built environment, and land use restriction variables. The results suggest that zoning can be tailored by cities to encourage reductions in auto usage, furthering sustainability goals in transportation.

Using Deep Learning to Understand Travel Demands in Different Urban Districts

2021 ◽  
pp. 036119812199458
Author(s):  
Shunhua Bai ◽  
Junfeng Jiao
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Prediction Accuracy ◽  
Travel Demand ◽  
Short Term Memory ◽  
Future Research ◽  
Demand Forecast ◽  
Time And Space ◽  
The University ◽  
The Impact

Travel demand forecast plays an important role in transportation planning. Classic models often predict people’s travel behavior based on the physical built environment in a linear fashion. Many scholars have tried to understand built environments’ predictive power on people’s travel behavior using big-data methods. However, few empirical studies have discussed how the impact might vary across time and space. To fill this research gap, this study used 2019 anonymous smartphone GPS data and built a long short-term memory (LSTM) recurrent neural network (RNN) to predict the daily travel demand to six destinations in Austin, Texas: downtown, the university, the airport, an inner-ring point-of-interest (POI) cluster, a suburban POI cluster, and an urban-fringe POI cluster. By comparing the prediction results, we found that: the model underestimated the traffic surge for the university in the fall semester and overestimated the demand for downtown on non-working days; the prediction accuracy for POI clusters was negatively related to their adjacency to downtown; and different POI clusters had cases of under- or overestimation on different occasions. This study reveals that the impact of destination attributes on people’s travel demand can vary across time and space because of their heterogeneous nature. Future research on travel behavior and built environment modeling should incorporate the temporal inconsistency to achieve better prediction accuracy.

scholarly journals IMPACT OF MEMENTO VALUE ON THE PERCEPTION OF CULTURAL HERITAGE: CASE STUDY OF THE MODERN MOVEMENT AND DIALECT OF KAUNAS / ATMINTIES VERTĖ SUVOKIANT KULTŪROS PAVELDĄ: MODERNIZMO JUDĖJIMO IR JO KAUNIETIŠKOJO DIALEKTO ATVEJO TYRIMAS

2018 ◽  
Vol 10 (0) ◽  
pp. 1-7
Author(s):  
Huriye Armagan DOGAN
Keyword(s):  
Built Environment ◽  
Cultural Heritage ◽  
Empirical Approach ◽  
The Past ◽  
Space And Time ◽  
The Impact ◽  
Modern Movement

Memento value in heritage is one of the most essential characteristics facilitating the association between the environment and its users, by connecting structures with space and time, moreover, it helps people to identify their surroundings. However, the emergence of the Modern Movement in the architectural sphere disrupted the reflection of memory and symbols which serve to root the society in its language. Furthermore, it generated an approach that stood against the practice of referring to the past and tradition, which led to the built environment becoming homogeneous and deprived of memento value. This paper focuses on the impact of memento value on the perception and evaluation of cultural heritage. Furthermore, it investigates the notions which are perceived to influence the appraisal of cultural heritage by applying them to the Kaunas dialect of the Modern Movement with an empirical approach.

scholarly journals Evacuation Data from a Hospital Outpatient Drill The Case Study of North Shore Hospital

2020 ◽  
Vol 5 ◽  
Author(s):  
Anass Rahouti ◽  
Ruggiero Lovreglio ◽  
Phil Jackson ◽  
Sélim Datoussaïd
Keyword(s):  
Built Environment ◽  
Fire Safety ◽  
Movement Time ◽  
Data Set ◽  
Healthcare Facilities ◽  
Staff Members ◽  
North Shore ◽  
The North ◽  
The Impact

Assessing the fire safety of buildings is fundamental to reduce the impact of this threat on their occupants. Such an assessment can be done by combining existing models and existing knowledge on how occupants behave during fires. Although many studies have been carried out for several types of built environment, only few of those investigate healthcare facilities and hospitals. In this study, we present a new behavioural data-set for hospital evacuations. The data was collected from the North Shore Hospital in Auckland (NZ) during an unannounced drill carried out in May 2017. This drill was recorded using CCTV and those videos are analysed to generate new evacuation model inputs for hospital scenarios. We collected pre-movement times, exit choices and total evacuation times for each evacuee. Moreover, we estimated pre-movement time distributions for both staff members and patients. Finally, we qualitatively investigated the evacuee actions of patients and staff members to study their interaction during the drill. The results show that participants were often independent from staff actions with a majority able to make their own decision.

New York’s War on Drugs and the Impact on Female Incarceration Rates

2020 ◽  
pp. 155708512095184
Author(s):  
Colleen D. Mair
Keyword(s):  
New York ◽  
War On Drugs ◽  
Unique Case ◽  
Drug Laws ◽  
Drug Legislation ◽  
Incarceration Rate ◽  
Rockefeller Drug Laws ◽  
Prior Literature ◽  
The Impact

Prior literature suggests that drug legislation in the late 1970s and 1980s caused the rapid increase in the female incarceration rate. Empirical investigations focused on the female incarceration rate specifically may provide important information to further our understanding of the factors that contributed to this increase. The purpose of this study is to determine how much of the change in the female incarceration rate in New York can be attributed to the introduction of the 1973 Rockefeller Drug Laws. These laws were introduced prior to most war on drugs legislation and, therefore, serve as a unique case study for this type of investigation.

scholarly journals Short-Term Prediction of Demand for Ride-Hailing Services: A Deep Learning Approach

2021 ◽  
Author(s):  
Long Chen ◽  
Piyushimita Vonu Thakuriah ◽  
Konstantinos Ampountolas
Keyword(s):  
New York ◽  
Deep Learning ◽  
Built Environment ◽  
Travel Behavior ◽  
Traffic Congestion ◽  
Service Providers ◽  
Road Traffic ◽  
Travel Behaviour ◽  
Demand Prediction ◽  
Research Areas

AbstractAs ride-hailing services become increasingly popular, being able to accurately predict demand for such services can help operators efficiently allocate drivers to customers, and reduce idle time, improve traffic congestion, and enhance the passenger experience. This paper proposes UberNet, a deep learning convolutional neural network for short-time prediction of demand for ride-hailing services. Exploiting traditional time series approaches for this problem is challenging due to strong surges and declines in pickups, as well as spatial concentrations of demand. This leads to pickup patterns that are unevenly distributed over time and space. UberNet employs a multivariate framework that utilises a number of temporal and spatial features that have been found in the literature to explain demand for ride-hailing services. Specifically, the proposed model includes two sub-networks that aim to encode the source series of various features and decode the predicting series, respectively. To assess the performance and effectiveness of UberNet, we use 9 months of Uber pickup data in 2014 and 28 spatial and temporal features from New York City. We use a number of features suggested by the transport operations and travel behaviour research areas as being relevant to passenger demand prediction, e.g., weather, temporal factors, socioeconomic and demographics characteristics, as well as travel-to-work, built environment and social factors such as crime level, within a multivariate framework, that leads to operational and policy insights for multiple communities: the ride-hailing operator, passengers, third-part location-based service providers and revenue opportunities to drivers, and transport operators such as road traffic authorities, and public transport agencies. By comparing the performance of UberNet with several other approaches, we show that the prediction quality of the model is highly competitive. Further, Ubernet’s prediction performance is better when using economic, social and built environment features. This suggests that Ubernet is more naturally suited to including complex motivators of travel behavior in making real-time demand predictions for ride-hailing services.

Spatial Clustering of Pedestrian Activity and the Built Environment Characteristics

Nano LIFE ◽  
2018 ◽  
Vol 08 (02) ◽  
pp. 1840005
Author(s):  
Hao Zhang ◽  
Li Yin
Keyword(s):  
New York ◽  
Built Environment ◽  
Spatial Patterns ◽  
Goodness Of Fit ◽  
Spatial Clustering ◽  
Poisson Model ◽  
Urban Revitalization ◽  
Policy Makers ◽  
Global And Local ◽  
The Impact

Promoting pedestrian activity has attracted increasing attention as an important strategy for the improvement of public health and urban revitalization. The impact on physical activity underpinned by built environment has been studied substantially; however, few studies had focused on the geographically varying relationships between pedestrian activity and the built environment characteristics. Built upon previous work, this study looks at the spatial patterns of pedestrian counts and the built environment contributors along two major streets in Buffalo, New York using global and local spatial autocorrelation tests and geographically weighted regression. Pedestrian generators, job density and land use mix are included as independent variables in order to study the impact on them due to the characteristics of built environment. Our findings suggest that (1) there are statistically significant clusters of street intersections with high pedestrian counts along the streets selected in our study; (2) there are some optimal sizes of clusters of pedestrian generators, which attract more pedestrians; (3) geographically weighted Poisson model helps to analyze the geographically varying relationships between the built environment and pedestrian activity with a more pronounced goodness of fit. This research contributes to the understanding of the spatial patterns of pedestrian activity and the geographically varying relationship between the built environment and pedestrian counts. Hopefully this research will help to guide and focus the minds of policy makers and urban planners alike to introduce street vitality through the modifications of the built environment, so as to improve the quality of life in their neighborhoods.

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