scholarly journals The impact of bus priority policies on peak commuters behavior: An agent-based modelling perspective

Filomat ◽  
2016 ◽  
Vol 30 (15) ◽  
pp. 4101-4110
Author(s):  
Shuai Ling ◽  
Wandi Hu ◽  
Yongjie Zhang
Keyword(s):  
Public Transportation ◽  
Simulation Method ◽  
Bus Priority ◽  
Peak Period ◽  
Transit System ◽  
Departure Time ◽  
Urban Public Transport ◽  
Departure Time Choice ◽  
The Right ◽  
The Impact

By using micro-simulation method and the BushCMosteller reinforcement learning model, this paper modeled the behavior of urban commuters departure time choice on a many-to-one transit system during the morning peak-period. Three kinds of typical urban public transport priority policies were studied. Result shows that if we can choose the right time for free public transportation, the pre-peak-free policy will have certain effects on staggering the commuting peak by influencing commuters decision making on departure-time. As for the bus-accelerating policy, it can lower commuters cost, but it is likely to cause more congested volume and add more pressure on the public transit system. The departure frequency increasing policy can partially alleviate the peak congestion problem, but cannot fundamentally eliminate the congestion, instead, it may increase the operating costs. This research is helpful in acquiring a better understanding of commuters departure time choice and commuting equilibrium during the peak period. The research approaches also provide an effective way to explore the formation and evolution of complicated traffic phenomena.

scholarly journals Agent-Based Modeling and Simulation for the Bus-Corridor Problem in a Many-to-One Mass Transit System

2014 ◽  
Vol 2014 ◽  
pp. 1-16
Author(s):  
Qinmu Xie ◽  
Shoufeng Ma ◽  
Ning Jia ◽  
Yang Gao
Keyword(s):  
Traffic Congestion ◽  
Evolutionary Dynamics ◽  
Mass Transit ◽  
Urban Traffic ◽  
Peak Period ◽  
Transit System ◽  
Departure Time ◽  
Departure Time Choice ◽  
Multiagent Modeling ◽  
Formation And Evolution

With the growing problem of urban traffic congestion, departure time choice is becoming a more important factor to commuters. By using multiagent modeling and the Bush-Mosteller reinforcement learning model, we simulated the day-to-day evolution of commuters’ departure time choice on a many-to-one mass transit system during the morning peak period. To start with, we verified the model by comparison with traditional analytical methods. Then the formation process of departure time equilibrium is investigated additionally. Seeing the validity of the model, some initial assumptions were relaxed and two groups of experiments were carried out considering commuters’ heterogeneity and memory limitations. The results showed that heterogeneous commuters’ departure time distribution is broader and has a lower peak at equilibrium and different people behave in different pattern. When each commuter has a limited memory, some fluctuations exist in the evolutionary dynamics of the system, and hence an ideal equilibrium can hardly be reached. This research is helpful in acquiring a better understanding of commuter’s departure time choice and commuting equilibrium of the peak period; the approach also provides an effective way to explore the formation and evolution of complicated traffic phenomena.

Departure Time Choice in Schedule-Based Transit Assignment

2021 ◽  
pp. 036119812110338
Author(s):  
Markus Friedrich ◽  
Matthias Schmaus ◽  
Jonas Sauer ◽  
Tobias Zündorf
Keyword(s):  
Travel Demand ◽  
Time Intervals ◽  
Transit Assignment ◽  
Common Assumption ◽  
Adaptation Time ◽  
Highly Sensitive ◽  
Departure Time ◽  
Departure Time Choice ◽  
Passenger Behavior ◽  
The Impact

This paper investigates existing departure time models for a schedule-based transit assignment and their parametrization. It analyzes the impact of the temporal resolution of travel demand and suggests functions for evaluating the adaptation time as part of the utility of a path. The adaptation time quantifies the time between the preferred and the scheduled departure times. The findings of the analysis suggested that travel demand should be discretized into intervals of 1 min, with interval borders right between the full minute, that is, ±0.5 min. It was shown that longer time intervals led to arbitrary run volumes, even for origin–destination pairs with just one transit line and a fixed headway. Although a linear relationship between adaptation time and adaptation disutility is a common assumption in several publications, it cannot represent certain types of passenger behavior. For some trip purposes, passengers may be insensitive to small adaptation times, but highly sensitive to large adaptations. This requires a nonlinear evaluation function.

scholarly journals On the Impact of HOT Lane Tolling Strategies on Total Traffic Level

2012 ◽  
Vol 48 (3) ◽  
Author(s):  
Soheil Sibdari ◽  
Mansoureh Jeihani
Keyword(s):  
Numerical Analysis ◽  
Dynamic Pricing ◽  
User Equilibrium ◽  
Pricing Strategies ◽  
Peak Period ◽  
Dynamic Strategy ◽  
Traffic Level ◽  
The Right ◽  
The Impact ◽  
High Occupancy Toll

This paper shows how tolling (or pricing) strategies can be used to control the congestion levels of both untolled and high occupancy toll (HOT) lanes. Using a user-equilibrium method, the paper calculates the number of travelers on each route during the peak period and provides a numerical analysis that determines the distribution of travelers for different tolling strategies. It shows that with the right tolling strategy some travelers who initially plan to use the untolled lane during the peak period will change both their routes (i.e., select the HOT lane) and departure times (i.e., depart earlier or later). Using this result, the paper compares static and dynamic pricing strategies and shows that with a dynamic strategy a larger profit can be earned and congestion reduced in the untolled lane.

Monte Carlo method for uncertainty evaluation of noise annoyance prevalence

2021 ◽  
Vol 263 (2) ◽  
pp. 3944-3952
Author(s):  
Ricardo Luís d'Avila Villela
Keyword(s):  
Monte Carlo ◽  
Noise Pollution ◽  
Simulation Method ◽  
Health Issues ◽  
Uncertainty Sources ◽  
Exposure Response ◽  
Density Maps ◽  
Noise Impact ◽  
The Right ◽  
The Impact

When a decision-making process relies on the information provided by a measurement or simulation result, the right decision demands a good quality result, in other words, a low uncertainty result. In order to establish public policies for environmental noise control, it is essential to identify the impact of each type of noise pollution (e.g. road, aircraft and rail transportation noise) on the population affected. One of the noise impact metrics that can be used is the number of highly noise annoyed people in a region whose estimated value is obtained from the corresponding exposure-response function and noise and population density maps. However, an estimated value of the noise impact metric with high uncertainty makes it difficult to realize the actual severity of the problem and its priority in relation to other public health issues. In this work, a Monte Carlo simulation method is used to assess the uncertainty of a noise impact metric result, namely the number of people highly disturbed by road noise in a city. This article also presents a sensitivity analysis of uncertainty sources that allows quantification of the main uncertainty components, which supports improvements in noise impact metric results.

scholarly journals Determination of Operational Efficiency in Urban Public Transport Lines

2021 ◽  
Vol 2 (1) ◽  
pp. 16-20
Author(s):  
Ahmet Atalay
Keyword(s):  
Public Transport ◽  
Public Transportation ◽  
Transportation Systems ◽  
Environmental Benefits ◽  
Urban Centers ◽  
Functional Activities ◽  
Functional Efficiency ◽  
Urban Public Transport ◽  
The Right ◽  
Public Transportation Systems

The increasing number of urban centers and the increasing number of vehicles caused by industrialization caused problems such as lack of infrastructure in traffic, environmental pollution and an increase in energy requirements. This situation led the city administrators to search for solutions in order to improve the efficiency of public transportation systems and increase their efficiency. In this study, it is aimed to determine the functional efficiency of the bus lines used in urban public transportation. For this purpose, the lines are classified according to their functional activities by using the functional data of the lines. Both classical cluster analysis and self-organizing mapping (SOM) method were used for classification. Data from Erzurum main public transport lines were used to implement the methods. According to the findings of this study, it was determined that the two methods achieved similar results. As a result, it has been determined that classification of public transportation lines used in cities according to their functional efficiency will be beneficial for decision makers to make correct planning. With the right planning in public transport lines, significant economic and environmental benefits will be obtained.

scholarly journals Rationalizing Urban Transportation using Smart Card Data

2019 ◽  
Vol 8 (11S) ◽  
pp. 507-515
Keyword(s):  
Smart Card ◽  
Public Transport ◽  
Public Transportation ◽  
Urban Population ◽  
Transport Systems ◽  
Living Standard ◽  
Transit System ◽  
Advantages And Disadvantages ◽  
Smart Card Data ◽  
The Impact

The urban population in 2014 accounted for 54% of the total global population, up from 34% in 1960, and continues to grow. The global urban population is expected to grow approximately 1.84%, 1.63% and 1.44% between 2015 and 2020, 2020 and 2025, and 2025 and 2030 respectively. This growing population puts pressure on government not only to accommodate the current and potential citizens but also provide them facilities and services for a better living standard. Providing a sustainable growing environment for the citizens is the biggest challenge for the government. As the populations increase, complexity network of transportation, water and sanitation, emergency services, etc. will increase many folds. SMART CITY Mission is being implemented to resolve this issue. As the cities turn smart, so should the transportation facilities. India on June 2018 had only 20 cities with populations of over 500,000 have organized public transport systems, pointing to the large gap to be bridged in their journey to turn smart. The aim of this paper is to examine the impact of smart card data from public transport for improving the predictions and planning of public transport usage and congestions. The mobile apps like M-Indicator, Google Maps don’t interlink, do not have a real time tracking of vehicles, fare distribution, congestion-based route mapping for public transportation. These factors are addressed in the paper with its advantages and disadvantages. This paper also talks about how information from smart card is to be extracted, how Big Data is to be managed and finally come to a smart, sustainable Urban Transit System. This paper also brings into light the data security issues and measures to curb those issues. This paper proposes and emphasizes on a single smart card for all modes of public transport

scholarly journals Old People’s Fear of COVID-19 Infection and Public Transportation Avoidance : Korean Subway Evidence

2021 ◽  
Author(s):  
Byungjin Park ◽  
Joonmo Cho
Keyword(s):  
Public Transportation ◽  
Public Transit ◽  
The Elderly ◽  
Fixed Effect Model ◽  
Capital City ◽  
Mass Transportation ◽  
Age Group ◽  
Transit System ◽  
Daily Lives ◽  
The Impact

Abstract Background: With the spread of the coronavirus worldwide, a principal policy implemented by nations was restrictions on movement of people. The effect of governments’ mobility restriction measures has been analyzed after the COVID-19 outbreak. However, there is lack of studies on the impact of voluntary restriction that significantly affects the decrease of the mobility. This research aims to analyze mass transportation use after the COVID-19 outbreak by age group to explore how the fear of infection affected the public transit system. Methods: Mass transportation big data of Seoul Metro transportation use in the capital city of South Korea was employed for a panel analysis. To control the environmental characteristics of each district of Seoul Metropolitan City, the fixed-effect model was employed. Results: The analysis results showed that in both the period of the highest infections and the period of the lowest infection of COVID-19, users aged 65 and over reduced subway use more than people aged between 20 and 64. The decrease of subway use caused by the sharp increase of COVID-19 cases was the most prominent among people aged 65 and over. The elasticity of change of subway use demand to change in cases in Seoul was about 0.08 for people aged 65 and over, higher than 0.06 for people aged between 20 and 64. Conclusion: The voluntary restrictions driven by fear of the COVID-19 infection have led to the decrease of public transit demand in Seoul. Although the subway use demand decreased both in the age group of 20 to 64 and the age group of people 65 and older, the elderly responded more sensitively to COVID-19. The results suggest that the fear of COVID-19 pandemic varies with age. It seems that the elderly’s higher death rate from COVID-19 has significantly impacted their behavioral change. This study imply that the elder’s fear of infection has affected their daily lives, consumption, and production activities and their mobility using public transportation.

Reshaping the Region

2015 ◽  
Vol 2537 (1) ◽  
pp. 33-41
Author(s):  
Stephanie Pollack ◽  
Anna Gartsman ◽  
Timothy Reardon ◽  
Meghna Hari
Keyword(s):  
Land Use ◽  
Public Transportation ◽  
Travel Behavior ◽  
Massachusetts Bay ◽  
Data Set ◽  
Transit System ◽  
Transit Systems ◽  
Commuter Rail ◽  
In Transit ◽  
The Impact

The American Public Transportation Association's use of a “land use multiplier” as part of its methodology for calculating greenhouse gas reduction from transit has increased interest in methodologies that quantify the impact of transit systems on land use and vehicle miles traveled. Such transit leverage, however, is frequently evaluated for urbanized areas, although transit systems serve only a small proportion of those areas. If transit leverage is stronger in areas closer to transit stations, studies based on larger geographies may underestimate land use and travel behavior effects in transit-served areas. A geographic information system–based data set was developed to understand better the leverage effects associated with the mature and extensive Massachusetts Bay Transportation Authority transit system in areas proximate to its stations throughout Metropolitan Boston. The region was divided into the subregion that was transit-proximate (within a half mile of a rapid transit station or key bus route), the portion that was commuter rail–proximate, and the remaining 93.3% of the region that was not proximate to high-frequency transit. Households in the transit-proximate subregion were significantly more likely to commute by transit (and walking or biking), less likely to own a car, and drove fewer miles than households in the non-transit-served areas of the region. Commuter rail–proximate areas, although denser than the region as a whole, exhibited more driving and car ownership than regional averages. Given these spatial and modal variations, future efforts to understand transit leverage should separately evaluate land use and travel effects by mode and proximity to transit stations.

scholarly journals Departure Time Choice (DTC) Behavior for Intercity Travel during a Long-Holiday in Bangkok, Thailand

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Bhawat Chaichannawatik ◽  
Kunnawee Kanitpong ◽  
Thirayoot Limanond
Keyword(s):  
Travel Behavior ◽  
Traffic Congestion ◽  
Travel Demand ◽  
Multinomial Logit Model ◽  
Time Of Day ◽  
Travel Patterns ◽  
Peak Period ◽  
Intercity Travel ◽  
Departure Time ◽  
Departure Time Choice

Time-of-day (TOD) or departure time choice (DTC) has become an interesting issue over two decades. Many researches have intensely focused on time-of-day or departure time choice study, especially workday departures. However, the travel behavior during long-holiday/intercity travel has received relatively little attention in previous studies. This paper shows the characteristics of long-holiday intercity travel patterns based on 2012 New Year data collected in Thailand with a specific focus on departure time choice of car commuters due to traffic congestion occurring during the beginning of festivals. 590 interview data were analyzed to provide more understanding of general characteristics of DTC behavior for intercity travel at the beginning of a Bangkok long-holiday. Moreover, the Multinomial Logit Model (MNL) was used to find the car-based DTC model. The results showed that travelers tend to travel at the peak period when the parameters of personal and household are not so significant, in contrast to the trip-related characteristics and holiday variables that play important roles in traveler decision on departure time choice. Finally, some policies to distribute travel demand and reduce the repeatable traffic congestion at the beginning of festivals are recommended.

An Empirical Analysis of Delhi - Mumbai Sector Flight Fares

2017 ◽  
Vol 4 (4) ◽  
pp. 60-78
Author(s):  
T. Godwin
Keyword(s):  
Revenue Management ◽  
Dynamic Pricing ◽  
Time Windows ◽  
Analysis Framework ◽  
Pricing Decisions ◽  
Departure Time ◽  
Departure Date ◽  
Reservation System ◽  
The Right ◽  
The Impact

Revenue management is the art and science of making the right product or service available to the right customer at the right time through the right channel at right price. Dynamic pricing plays a crucial role in the implementation of revenue management in passenger airline reservation system. The liberalization of domestic aviation sector in countries such as India has seen many new market entrants resulting in higher competition while setting the flight fares. The variation in flight fares of Delhi – Mumbai passenger airline sector is studied for a departure date based on the number of days in advance the booking is made. Descriptive and inferential statistical analyses of the fares reveal the impact of airlines, booking channels and departure time windows on the pricing decisions of flight fares. The analysis framework of this study could be used as a basis for a continuous tracking study of flight fares by airline revenue managers to help them arrive at the right fare for each fare class of a flight.

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