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.

Transit Planning Optimization Under Ride-Hailing Competition and Traffic Congestion

2021 ◽  
Author(s):  
Keji Wei ◽  
Vikrant Vaze ◽  
Alexandre Jacquillat
Keyword(s):  
New York ◽  
Traffic Congestion ◽  
Service Providers ◽  
Public Transit ◽  
Mixed Integer ◽  
Nonlinear Program ◽  
Transit Planning ◽  
Planning Optimization ◽  
Transit Ridership ◽  
Transit Agencies

With the soaring popularity of ride-hailing, the interdependence between transit ridership, ride-hailing ridership, and urban congestion motivates the following question: can public transit and ride-hailing coexist and thrive in a way that enhances the urban transportation ecosystem as a whole? To answer this question, we develop a mathematical and computational framework that optimizes transit schedules while explicitly accounting for their impacts on road congestion and passengers’ mode choice between transit and ride-hailing. The problem is formulated as a mixed integer nonlinear program and solved using a bilevel decomposition algorithm. Based on computational case study experiments in New York City, our optimized transit schedules consistently lead to 0.4%–3% system-wide cost reduction. This amounts to rush-hour savings of millions of dollars per day while simultaneously reducing the costs to passengers and transportation service providers. These benefits are driven by a better alignment of available transportation options with passengers’ preferences—by redistributing public transit resources to where they provide the strongest societal benefits. These results are robust to underlying assumptions about passenger demand, transit level of service, the dynamics of ride-hailing operations, and transit fare structures. Ultimately, by explicitly accounting for ride-hailing competition, passenger preferences, and traffic congestion, transit agencies can develop schedules that lower costs for passengers, operators, and the system as a whole: a rare win–win–win outcome.

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

scholarly journals How does the Built Environment Influence Public Transit Choice in Urban Villages in China?

2018 ◽  
Vol 11 (1) ◽  
pp. 148 ◽  
Author(s):  
Le Yu ◽  
Binglei Xie ◽  
Edwin Chan
Keyword(s):  
Built Environment ◽  
Low Income ◽  
Traffic Congestion ◽  
Travel Demand ◽  
Public Transit ◽  
Multinomial Logistic Regression ◽  
Large Population ◽  
Travel Behaviour ◽  
New Perspective ◽  
Urban Villages

With growing traffic congestion and environmental issues, the interactions between travel behaviour and the built environment have drawn attention from researchers and policymakers to take effective measures to encourage more sustainable travel modes and to curb car trips, especially in urbanising areas where travel demand is very complicated. This paper presents how built environmental factors affect public transit choice behaviour in urban villages in China, where a large population of low-income workers are accommodated. This location had a high demand for public transit and special built environmental characteristics. Multinomial logistic regression was employed to examine both the determinants and magnitude of their influence. The results indicate that the impacts of built environments apply particularly in urban villages compared to those in formal residences. In particular, mixed land use generates an adverse effect on public transit choice, a surprising outcome which is contrary to previous common conclusions. This study contributes by addressing a special type of neighbourhood in order to narrow down the research gap in this domain. The findings help to suggest effective measures to satisfy public transit demand efficiently and also provide a new perspective for urban regeneration.

scholarly journals Exploring the Effects of Urban Built Environment on Road Travel Speed Variability with a Spatial Panel Data Model

2021 ◽  
Vol 10 (12) ◽  
pp. 829
Author(s):  
Guangyue Nian ◽  
Jian Sun ◽  
Jianyun Huang
Keyword(s):  
Built Environment ◽  
Data Model ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Travel Speed ◽  
Road Segment ◽  
Key Factors ◽  
Spatial Panel ◽  
The Road ◽  
Spatial Panel Data

Road traffic congestion is a common problem in most large cities, and exploring the root causes is essential to alleviate traffic congestion. Travel behavior is closely related to the built environment, and affects road travel speed. This paper investigated the direct effect of built environment on the average travel speed of road traffic. Taxi trajectories were divided into 30 min time slot (48 time slots throughout the day) and matched to the road network to obtain the average travel speed of road segments. The Points of Interest (POIs) in the buffer zone on both sides of the road segment were used to calculate the built environment indicators corresponding to the road segment, and then a spatial panel data model was proposed to assess the influence of the built environment adjacent to the road segment on the average travel speed of the road segment. The results demonstrated that the bus stop density, healthcare service density, sports and leisure service density, and parking entrance and exit density are the key factors that positively affect the average road travel speed. The residential community density and business building density are the key factors that negatively affect the average travel speed. Built environments have spatial correlation and spatial heterogeneity in their influence on the average travel speed of road segments. Findings of this study may provide useful insights for understanding the correlation between road travel speed and built environment, which would have important implications for urban planning and governance, traffic demand forecasting and traffic system optimization.

scholarly journals Effects of demolishing abandoned buildings on firearm violence: a moderation analysis using aerial imagery and deep learning

2021 ◽  
pp. injuryprev-2021-044412
Author(s):  
Jonathan Jay ◽  
Jorrit de Jong ◽  
Marcia P Jimenez ◽  
Quynh Nguyen ◽  
Jason Goldstick
Keyword(s):  
New York ◽  
Deep Learning ◽  
Built Environment ◽  
Aerial Imagery ◽  
Long Term Effects ◽  
Firearm Violence ◽  
Units Of Analysis ◽  
Novel Approach ◽  
Incident Rate

PurposeDemolishing abandoned buildings has been found to reduce nearby firearm violence. However, these effects might vary within cities and across time scales. We aimed to identify potential moderators of the effects of demolitions on firearm violence using a novel approach that combined machine learning and aerial imagery.MethodsOutcomes were annual counts of fatal and non-fatal shootings in Rochester, New York, from 2000 to 2020. Treatment was demolitions conducted from 2009 to 2019. Units of analysis were 152×152 m grid squares. We used a difference-in-differences approach to test effects: (A) the year after each demolition and (B) as demolitions accumulated over time. As moderators, we used a built environment typology generated by extracting information from aerial imagery using convolutional neural networks, a deep learning approach, combined with k-means clustering. We stratified our main models by built environment cluster to test for moderation.ResultsOne demolition was associated with a 14% shootings reduction (incident rate ratio (IRR)=0.86, 95% CI 0.83 to 0.90, p<0.001) the following year. Demolitions were also associated with a long-term, 2% reduction in shootings per year for each cumulative demolition (IRR=0.98, 95% CI 0.95 to 1.00, p=0.02). In the stratified models, densely built areas with higher street connectivity displayed following-year effects, but not long-term effects. Areas with lower density and larger parcels displayed long-term effects but not following-year effects.ConclusionsThe built environment might influence the magnitude and duration of the effects of demolitions on firearm violence. Policymakers may consider complementary programmes to help sustain these effects in high-density areas.

scholarly journals Evaluating the Traffic and Emissions Impacts of Congestion Pricing in New York City

2020 ◽  
Vol 12 (9) ◽  
pp. 3655 ◽  
Author(s):  
Amirhossein Baghestani ◽  
Mohammad Tayarani ◽  
Mahdieh Allahviranloo ◽  
H. Oliver Gao
Keyword(s):  
New York ◽  
Travel Behavior ◽  
Traffic Congestion ◽  
Network Performance ◽  
Travel Demand ◽  
Transportation Network ◽  
Congestion Pricing ◽  
Central Business District ◽  
Demand Model ◽  
Business District

Traffic congestion is a major challenge in metropolitan areas due to economic and negative health impacts. Several strategies have been tested all around the globe to relieve traffic congestion and minimize transportation externalities. Congestion pricing is among the most cited strategies with the potential to manage the travel demand. This study aims to investigate potential travel behavior changes in response to cordon pricing in Manhattan, New York. Several pricing schemes with variable cordon charging fees are designed and examined using an activity-based microsimulation travel demand model. The findings demonstrate a decreasing trend in the total number of trips interacting with the central business district (CBD) as the price goes up, except for intrazonal trips. We also analyze a set of other performance measures, such as Vehicle-Hours of Delay, Vehicle-Miles Traveled, and vehicle emissions. While the results show considerable growth in transit ridership (6%), single-occupant vehicles and taxis trips destined to the CBD reduced by 30% and 40%, respectively, under the $20 pricing scheme. The aggregated value of delay for all vehicles was also reduced by 32%. Our findings suggest that cordon pricing can positively ameliorate transportation network performance and consequently, improve air quality by reducing particular matter inventory by up to 17.5%. The results might facilitate public acceptance of cordon pricing strategies for the case study of NYC. More broadly, this study provides a robust framework for decision-makers across the US for further analysis on the subject.

scholarly journals The Impact of Digital Navigation on Travel Behaviour

2021 ◽  
Author(s):  
David Metz
Keyword(s):  
Traffic Congestion ◽  
Road Network ◽  
Road Traffic ◽  
Travel Behaviour ◽  
Time Of Arrival ◽  
Route Guidance ◽  
The Road ◽  
Satellite Positioning ◽  
Available Information ◽  
The Impact

Digital navigation – the combined use of satellite positioning, digital mapping and route guidance – is in wide use for road travel yet its impact is little understood. Evidence is emerging of significant changes in use of the road network, including diversion of local trips to new major road capacity and increased use of minor roads, which have problematic implications for investment decisions and for the management of the network. However, the ability of digital navigation to predict estimated time of arrival under expected traffic conditions is a welcome means of mitigating journey time uncertainty, which is one of the undesirable consequences of road traffic congestion. There is very little available information about the impact of digital navigation on travel behaviour, a situation that needs to be remedied to enhance the efficiency of road network operation.Digital navigation – the combined use of satellite positioning, digital mapping and route guidance – is in wide use for road travel yet its impact is little understood. Evidence is emerging of significant changes in use of the road network, including diversion of local trips to new major road capacity and increased use of minor roads, which have problematic implications for investment decisions and for the management of the network. However, the ability of digital navigation to predict estimated time of arrival under expected traffic conditions is a welcome means of mitigating journey time uncertainty, which is one of the undesirable consequences of road traffic congestion. There is very little available information about the impact of digital navigation on travel behaviour, a situation that needs to be remedied to enhance the efficiency of road network operation.

Railway's Impacts on Modal Shift Potential Towards Intermodal Transportation: A Case Study in Lao PDR.

2020 ◽  
Vol 11 (1) ◽  
pp. 123-123
Author(s):  
Klairung Ponanan ◽  
Wachira Wichitphongsa
Keyword(s):  
Service Providers ◽  
Lao Pdr ◽  
Travel Behaviour ◽  
Silk Road ◽  
Transport Infrastructure ◽  
Chinese Government ◽  
Modal Shift ◽  
Long Distance ◽  
Silk Road Economic Belt ◽  
The Silk Road

Chinese government has developed transport infrastructure rapidly under Belt and Road Initiative (BRI) strategy. The BRI strategy is China's economic development strategies for expanding trade and cultural influence towards countries in western and eastern regions, including ASEAN. The development of BRI strategy is consists of two main components i.e., (i) the Silk Road Economic Belt, follows the historical overland Silk Road through Central Asia, Iran, Turkey and eventually to Europe, and (ii) the Maritime Silk Road, originates in the South China Sea, passing through the Malacca Strait, the Indian Ocean, and the Red Sea and extending into the Mediterranean Sea (Chris & Elizabeth, 2015). Due to the BRI strategy, more than 6000 trains made the journey from China to Europe in 2018, which is an increase of 72% compared to 2017. China has sent more than 11,000 freight trains to Europe and back since the BRI strategy was announced in 2013. Railway networks have been constructed under the BRI strategy for connecting 48 Chinese cities with 42 cities in Europe through Asia. There are many railway infrastructures under the BRI strategy. The China – Laos railway (Vientiane–Boten railway) is one of project under the Silk Road Economic Belt that has been developed for serving as a key infrastructure for the economic corridor between the two countries. In nearly future, this railway will be helped to boost trade, investment and tourism for Lao PDR. and south China's Guangxi Zhuang Autonomous Region. The Vientiane–Boten railway, especially transportation time will attract both travelers and Logistics Service Providers (LSP), which can be reduced time of journey compared with road mode. In this paper, modal shift potential of travelers and freight on Kunming-Bangkok Highway (R3A), AH2, AH8, AH9, AH10, AH12, AH13, and AH18 have been investigated by considering behavioral aspects of long distance travel. Keywords: Mode Split Model, Modal Shift, Vientiane–Boten railway, Travel Behaviour

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