scholarly journals The Morning Commute Problem with Ridesharing When Meet Stochastic Bottleneck

2021 ◽  
Vol 13 (11) ◽  
pp. 6040
Author(s):  
Zipeng Zhang ◽  
Ning Zhang
Keyword(s):  
Cost Analysis ◽  
Dynamic Property ◽  
Travel Behavior ◽  
Traffic Congestion ◽  
Related Research ◽  
Work Pattern ◽  
Schedule Coordination ◽  
Queue Model ◽  
Morning Commute ◽  
Analysis Models

This paper extends Vickrey’s point-queue model to study ridesharing behavior during a morning commute with uncertain bottleneck location. Unlike other ridesharing cost analysis models, there are two congestion cases and four dynamic departure patterns in our model: pre-pickup congestion case and post-pickup congestion case; both early pattern, both late pattern, late for pickup but early for work pattern, and early for pickup but late for work pattern. Analytical results indicate that the dynamic property of the mixed commuters equilibrium varies with the endogenous penetration rates associated with ridesharing commutes, as well as the schedule difference between pickup and work. This work is expected to promote the development of ridesharing to mitigate the traffic congestion and motivate related research of schedule coordination for regulating the ridesharing travel behavior in terms of the morning commute problem.

scholarly journals Early Bird Scheme for Parking Management: How Does Parking Play a Role in the Morning Commute Problem

2021 ◽  
Vol 13 (15) ◽  
pp. 8531
Author(s):  
Zipeng Zhang ◽  
Ning Zhang
Keyword(s):  
Traffic Congestion ◽  
Travel Demand ◽  
User Equilibrium ◽  
Analysis Model ◽  
Early Bird ◽  
Schedule Coordination ◽  
Parking Management ◽  
Morning Commute ◽  
Bottleneck Capacity ◽  
Parking Pricing

This paper extended the Vickrey’s point-queue model to study the early bird parking mechanism during morning commute peak hours. We not only investigated how commuters choose departure times in view of morning commute traffic congestion and the discounted early bird parking fee, but also analyzed the conditions which are determined for the existence of the user equilibrium in the analysis model provided in this paper. Moreover, the tendency of the total queuing time and the incremental parking pricing revenue was derived along with the different choice strategy between early bird parkers (ERPs) and regular parkers (RPs). The results showed that the number of commuters was jointly determined by the desired time and the bottleneck capacity for different schedules. Additionally, the method of fare incentive showed a better effect on reducing queue than the initial no-incentive method with the instantaneous travel demand. Most importantly, the incremental parking revenue can be increased by properly adjusting the parking pricing gap between ERPs and RPs. Our research not only provided several important propositions for the early bird parking mechanism but also included the optimal solutions for optimal parking pricing and schedule gap in two groups of parkers. This work is expected to promote the development of early bird parking to mitigate morning commute traffic congestion and motivate the related research of schedule coordination for regulating parking choice behavior in morning peak hours.

scholarly journals Research on an intelligent traffic congestion control model

2021 ◽  
Vol 336 ◽  
pp. 07001
Author(s):  
Bo Xu ◽  
Jianbing Chen ◽  
Wei Tang
Keyword(s):  
Congestion Control ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Control Model ◽  
Status Quo ◽  
Key Technology ◽  
Related Research ◽  
The Status ◽  
Traffic Congestion Control

This paper summarizes the status quo of intelligent traffic congestion control and vehicle following on traffic road, puts forward the key technology model and its content of intelligent traffic control, elaborates the model and content in detail, and summarizes the research done, hoping to provide reference for the related research on intelligent traffic congestion control.

scholarly journals PEMILIHAN LOKASI BELANJA DAN PERILAKU PERJALANAN DI KOTA BANDUNG

2019 ◽  
Vol 19 (1) ◽  
pp. 67-76
Author(s):  
Muhamad Rizki Muhamad ◽  
Tri Basuki Joewono
Keyword(s):  
Travel Behavior ◽  
Traffic Congestion ◽  
Grocery Shopping ◽  
Location Decision ◽  
Positive Interaction ◽  
Location Selection ◽  
Trip Chain ◽  
Similar Region ◽  
Shopping Location ◽  
Selection Of

Abstract Grocery shopping activities play substantial role in fulfilling individuals’ daily-activities needs. Its spatial-separated location characteristics led to travel consequence. Grocery shopping activities and travels still partly contributed to several challenges on urban transportation, such as traffic congestion. This study aims to investigate the shopping location decision as a part of shoppers’ spatial attribute. The analysis conducted based on data from a survey in Bandung City in 2017 which the shopping activities and travel characteristics were recorded. Result of analyses using classification analysis confirmed the hypotheses that there is positive interaction between travel characteristics, activity arrangements, and spatial conditions in selecting shopping location. It is found, similar region shopping location with resident area not necessarily lead to a choice. The study finds that the attractions offered by shopping locations influence location selection. In addition, the activity arrangement of shoppers through the trip chain were also found to influence the selection of shopping locations. Keywords: grocery shopping, shopping location, travel characteristics, travel behavior  Abstrak Kegiatan belanja sehari-hari berperan penting dalam memenuhi kebutuhan aktivitas sehari-hari individu maupun rumah tangga. Karakteristik spasial lokasi belanja mengakibatkan konsekuensi pada perjalanan. Kegiatan dan perjalanan belanja sehari-hari berkontribusi pada permasalahan transportasi perkotaan. Penelitian ini bertujuan untuk menganalisis keputusan pemilihan lokasi belanja sebagai bagian dari atribut spasial pelaku belanja. Analisis dilakukan berdasarkan data survei wawancara di Kota Bandung tahun 2017 tentang kegiatan belanja dan karakteristik perjalanan. Hasil analisis menggunakan analisis klasifikasi mendukung hipotesis bahwa ada interaksi positif antara karakteristik perjalanan, pengaturan aktivitas, dan kondisi spasial dalam memilih jenis lokasi belanja. Kesamaan lokasi belanja dan lokasi tempat tinggal tidak selalu mengarahkan pelaku belanja untuk memilih lokasi tersebut. Studi ini menemukan pula bahwa atraksi yang ditawarkan oleh lokasi belanja memengaruhi pemilihan tersebut. Selain itu, pengaturan aktivitas pelaku belanja juga ditemukan memengaruhi pemilihan lokasi belanja, yaitu dengan melakukan pengaturan perjalanan dan aktivitas melalui rantai perjalanan. Kata-kata kunci: belanja kebutuhan sehari-hari, lokasi belanja, karakteristik perjalanan, perilaku perjalanan

The Worst of All Worlds

2005 ◽  
Vol 1902 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Eric Eidlin
Keyword(s):  
Los Angeles ◽  
Population Density ◽  
Travel Behavior ◽  
Traffic Congestion ◽  
Population Distribution ◽  
Housing Prices ◽  
The United States ◽  
Urban Life ◽  
Apparent Paradox ◽  
Gini Coefficients

Los Angeles, California, is generally considered the archetypal sprawling metropolis. Yet traditional measures equate sprawl with low population density, and Los Angeles is among the densest and thereby the least sprawling cities in the United States. How can this apparent paradox be explained? This paper argues that the answer lies in the fact that Los Angeles exhibits a comparatively even distribution of population throughout its urbanized area. As a result, the city suffers from many consequences of high population density, including extreme traffic congestion, poor air quality, and high housing prices, while offering its residents few benefits that typically accompany this density, including fast and effective public transit, vibrant street life, and tightly knit urban neighborhoods. The city's unique combination of high average population density with little differentiation in the distribution of population might best be characterized as dense sprawl, a condition that embodies the worst of urban and suburban worlds. This paper uses Gini coefficients to illustrate variation in population density and then considers a number of indicators–-most relating either to the provision of transportation infrastructure or to travel behavior–-that demonstrate the effects of low-variation population distribution on the quality of urban life in Los Angeles. This approach offers researchers, practitioners, and policy makers in Los Angeles and in smaller cities that are evolving in similar ways a useful and user-friendly tool for identifying, explaining, measuring, and addressing the most problematic aspects of sprawl.

scholarly journals Suburban Employment Centers: Probing the Influence of Site Features on the Journey-to-Work

1989 ◽  
Vol 8 (2) ◽  
pp. 75-85 ◽  
Author(s):  
Robert Cervero
Keyword(s):  
Land Use ◽  
Travel Behavior ◽  
Traffic Congestion ◽  
Low Density ◽  
Traffic Conditions ◽  
Employment Centers ◽  
Single Use ◽  
Journey To Work ◽  
Levels Of Service ◽  
The U.S

This paper argues that the low-density, single-use character of most suburban workplaces in the U.S. has contributed to worsening traffic congestion by making most workers highly dependent on their own automobiles for accessing jobs. To test this proposition, land use and transportation data are examined for fifty of the largest suburban employment centers in the nation. Differences in the share of trips made by various modes, commuting speeds, and levels of service on major thoroughfares connecting suburban centers are compared among clusters of centers. The densities, sizes, and land use mixtures of suburban workplaces are generally found to be important determinants of worker travel behavior and local traffic conditions.

Effects of Traffic Congestion on Vehicle Miles Traveled

2018 ◽  
Vol 2672 (47) ◽  
pp. 92-102 ◽  
Author(s):  
Reza Sardari ◽  
Shima Hamidi ◽  
Raha Pouladi
Keyword(s):  
Travel Behavior ◽  
Traffic Congestion ◽  
Structural Equation ◽  
National Level ◽  
Network Connectivity ◽  
Transportation Systems ◽  
Equation Modeling ◽  
Population Mobility ◽  
Vehicle Miles Traveled ◽  
Land Use Patterns

The effects of traffic congestion on travel behavior are complex and multidimensional because they are related to various factors such as density, land use patterns, network connectivity, and individual preferences. Traffic congestion is a phenomenon that not only affects transportation systems but also influences commuters’ quality of life and population mobility. The present research aims to analyze the effects of traffic congestion on individuals’ travel behaviors, addressing both direct and indirect effects of congestion on vehicle miles traveled (VMT) per driver by implementing structural equation modeling (SEM) techniques. In addition to the causal analysis between traffic congestion and VMT, this study examined the complex relationship between an individual’s socioeconomic characteristics, the built environment, congestion, and VMT. Measuring local congestion at a national level is also a key contribution of this research. This study used the same methodology as the Texas A&M Transportation Institute to compute a road congestion index and quantify local congestion for 93,769 drivers within 337 metropolitan areas. Our findings suggest that congestion is the main driver of VMT reduction. The findings also confirm that residents in compact development regions have lower daily VMTs because of the proximity of origins and destinations in denser areas with higher job–population balances. Therefore, rather than expanding highway networks, public transit investment might address traffic congestion more efficiently—not only by providing residents with more equitable and sustainable means of transportation, but also by encouraging people to reside in more compact and location-efficient areas.

scholarly journals Exploring the Influence of Attitudes to Walking and Cycling on Commute Mode Choice Using a Hybrid Choice Model

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Chuan Ding ◽  
Yu Chen ◽  
Jinxiao Duan ◽  
Yingrong Lu ◽  
Jianxun Cui
Keyword(s):  
Travel Behavior ◽  
Traffic Congestion ◽  
Structural Equation ◽  
Choice Model ◽  
Mode Choice ◽  
Influential Factors ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
The Impact ◽  
Hybrid Choice Model

Transport-related problems, such as automobile dependence, traffic congestion, and greenhouse emissions, lead to a great burden on the environment. In developing countries like China, in order to improve the air quality, promoting sustainable travel modes to reduce the automobile usage is gradually recognized as an emerging national concern. Though there are many studies related to the physically active modes (e.g., walking and cycling), the research on the influence of attitudes to active modes on travel behavior is limited, especially in China. To fill up this gap, this paper focuses on examining the impact of attitudes to walking and cycling on commute mode choice. Using the survey data collected in China cities, an integrated discrete choice model and the structural equation model are proposed. By applying the hybrid choice model, not only the role of the latent attitude played in travel mode choice, but also the indirect effects of social factors on travel mode choice are obtained. The comparison indicates that the hybrid choice model outperforms the traditional model. This study is expected to provide a better understanding for urban planners on the influential factors of green travel modes.

scholarly journals SOLVING TRAFFIC CONGESTION FROM THE DEMAND SIDE

2015 ◽  
Vol 27 (6) ◽  
pp. 529-538 ◽  
Author(s):  
Ying-En Ge ◽  
Olegas Prentkovskis ◽  
Chunyan Tang ◽  
Wafaa Saleh ◽  
Michael G. H. Bell ◽  
...  
Keyword(s):  
Travel Behavior ◽  
Traffic Congestion ◽  
Travel Demand ◽  
State Of The Art ◽  
Demand Management ◽  
Relevant Literature ◽  
Short Review ◽  
Active Management ◽  
Demand Side ◽  
Travel Demand Management

It is nowadays widely accepted that solving traffic congestion from the demand side is more important and more feasible than offering more capacity or facilities for transportation. Following a brief overview of evolution of the concept of Travel Demand Management (TDM), there is a discussion on the TDM foundations that include demand-side strategies, traveler choice and application settings and the new dimensions that ATDM (Active forms of Transportation and Demand Management) bring to TDM, i.e. active management and integrative management. Subsequently, the authors provide a short review of the state-of-the-art TDM focusing on relevant literature published since 2000. Next, we highlight five TDM topics that are currently hot: traffic congestion pricing, public transit and bicycles, travel behavior, travel plans and methodology. The paper closes with some concluding remarks.

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.

scholarly journals Promoting Product Service System for Motorcycle: A Study of Ridesharing Program Intention for University Students

2019 ◽  
Vol 18 (2) ◽  
pp. 142-152
Author(s):  
Azis Slamet Wiyono ◽  
Septin Puji Astuti
Keyword(s):  
Travel Behavior ◽  
Traffic Congestion ◽  
Motor Vehicle ◽  
Product Service System ◽  
Environmental Destruction ◽  
Driving Licence ◽  
Home Address ◽  
Product Service ◽  
Air Pollutions ◽  
Student Travel

The need to reduce air pollutions produced by motor vehicle and traffic congestion is prominent as it improves human health and environmental destruction mitigation. Ridesharing programme is an effort to reduce traffic congestion without preventing people doing mobility and prohibiting them to buy motor vehicle. This paper addresses to investigate the determinant of university student to ridesharing programme. Four variables with descrete items were provided t respondents to be self selected. Those are home address status, type of driving licence, student’s travel behavior to university and intention of student to ridesharing programme. Those variables are analysed in to three models. By using Logistic Regression statictical analysis, this study shows that ridesharing programme intention is influenced by the behavior of student to travel to university. Meanwhile, student travel behavior is influenced by home address status and the ownership of driving licence of students.

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