How Congested Jakarta is? Perception of Jakarta’s Citizen on Traffic Congestion

This paper aims to reveal the behavior and perception of Jakarta’s citizens on traffic congestion in Jakarta. Although this approach is somewhat well-developed in behavioral science, its utilization in urban economics study, is still limited. Detecting the traffic congestion and its cause mainly relies on physical (engineering) methods, i.e V/C ratio. Here, we define the traffic congestion through two variables; ordinal traffic congestion perception and proportion of expected travel time to perceived travel time. Using a non-probabilistic sampling survey held in one of densest business district in Jakarta called Sudirman-Thamrin Golden Triangle Area; the estimation results show that travel behavior plays a major role in affecting travel time perceptions.AbstrakStudi ini bertujuan untuk melihat tingkah laku masyarakat Jakarta terhadap kemacetan di Jakarta. Pendekatan yang digunakan dalam studi ini telah banyak dikembangkan dalam studi behavioral science, namun penggunaanya dalam studi ekonomi perkotaan masih terbatas. Mendeteksi tingkat kemacetan serta penyebabnya umumnya mengandalkan metode fisik seperti V/C ratio. Studi ini mendefinisikan tingkat kemacetan ke dalam dua variabel, persepsi tingkat kemacetan ordinasl serta proporsi dari ekspektasi waktu perjalanan terhadap waktu perjalanan actual. Dengan menggunakan survey non-probabilitic sampling di Sudirman-Tharim Golden Triangle Area, hasil estimasi menunjukkan bahwa perilaku perjalanan (travel behavior) berperan utama dalam mempengaruhi persepsi waktu perjalanan.Kata kunci: Tingkat Kemacetan; Waktu Perjalanan; Perilaku Perjalanan; PersepsiJEL classifications: R40; R41

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Impaction of Travel Behavior and Degree of Business District Prosperity on Shopping

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.488-489.1426 ◽

2014 ◽

Vol 488-489 ◽

pp. 1426-1429

Author(s):

Ming Wei Liu ◽

Shou Qi Cao ◽

Li Zhen Zhang ◽

Cheng Ming Chen

Keyword(s):

Travel Time ◽

Travel Behavior ◽

Choice Behavior ◽

Store Choice ◽

Media Markets ◽

Business District

Three Media-Markets located in Shanghai have been studied the research in this area to quantitatively analysis how transportation factors such as travel time, transportation method, distance, cost and the degree of business districts prosperity affect store choice behavior.

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Optimizing route choice in multimodal transportation networks

Canadian Journal of Civil Engineering ◽

10.1139/cjce-2013-0331 ◽

2014 ◽

Vol 41 (9) ◽

pp. 800-810 ◽

Cited By ~ 1

Author(s):

Behzad Rouhieh ◽

Ciprian Alecsandru

Keyword(s):

Public Transportation ◽

Travel Behavior ◽

Traffic Congestion ◽

Transportation Networks ◽

Routing Algorithm ◽

Central Business District ◽

Subsequent Work ◽

Traveler Information ◽

Multimodal Transportation ◽

Business District

Advanced traveler information systems provide travelers with pre-trip and en route travel information necessary to improve the trip decision making process based on various criteria (e.g., avoiding the negative impacts of traffic congestion, selecting specific travel modes, etc.). This study investigates an adaptive routing methodology for multimodal transportation networks. To integrate transit networks, the model takes into account both the predefined timetables of public transportation services and the variability of travel times. A graph theory based methodology is proposed to capture travel behavior within a multimodal network. The study advances a routing algorithm based on Markov decision processes. Special network modeling elements were defined to allow the developed algorithm to select the most efficient transportation mode at each junction along a given route. The proposed methodology is applied to a small real-world network located in the central business district area of Montreal, Quebec. The network includes bus, subway, and bicycle transportation facilities. The simulations were run under the assumption that users do not use private vehicles to travel between arbitrary selected origin and destination points. The developed routing algorithm was applied to several simulation scenarios. The results identified what is the most efficient combination of transportation modes that the travelers have to use given certain traffic and transit service conditions. Larger and more complex networks of motorized and non-motorized modes with stochastic properties will be investigated in subsequent work.

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

Sustainability ◽

10.3390/su12093655 ◽

2020 ◽

Vol 12 (9) ◽

pp. 3655 ◽

Cited By ~ 8

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.

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Travel Behavior of Car Travelers with the Presence of Park-and-Ride Facilities and Autonomous Vehicles

Periodica Polytechnica Transportation Engineering ◽

10.3311/pptr.18020 ◽

2021 ◽

Author(s):

Jamil Hamadneh ◽

Domokos Esztergár-Kiss

Keyword(s):

Travel Time ◽

Travel Behavior ◽

Traffic Congestion ◽

Autonomous Vehicles ◽

Daily Activity ◽

Policy Makers ◽

Park And Ride ◽

People Search ◽

Parking Pricing ◽

The Impact

Travelers' behavior is predicted based on their individual preferences. People search for alternatives to maximize their benefit from doing activities, such as increasing the activity time by minimizing the travel time. Traffic congestion and the scarcity of parking spaces in the city center motivate the decision-makers to encourage travelers to use the park-and-ride (P&R) system. An evaluation concerning the impact of using the P&R system on the travel behavior of car users is conducted. Some of the existing P&R facilities are incorporated into the daily activity plans of car travelers to produce new daily activity plans (i.e., P&R facility is considered an activity). By using the Multi-Agent Transport Simulation (MATSim) open-source tool, simulations of the daily activity plans including the P&R system and autonomous vehicles (AVs) are conducted. The study examines three scenarios: (1) a simulation of the existing condition, (2) a simulation of the daily activity plans of the travelers with the P&R system, and (3) a simulation of the daily activity plans of the travelers with the P&R system and AVs. The results show that using the P&R system increases the overall travel time compared with the existing conditions, and the use of AVs as a transport mode impacts the existing modal share as follows: 64 % of the car users switch to AVs, while 15 % of the car users switch to public transport. The output of this study might be used by policy-makers in parking pricing and the location of the P&R facilities.

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Values of Travel Time for Recreational Trips under Different Behavioural Rules

Sustainability ◽

10.3390/su13126831 ◽

2021 ◽

Vol 13 (12) ◽

pp. 6831

Author(s):

Rosa Marina González ◽

Concepción Román ◽

Ángel Simón Marrero

Keyword(s):

Travel Time ◽

National Parks ◽

Traffic Congestion ◽

National Park ◽

Revealed Preference ◽

Discrete Choice Models ◽

Model Specification ◽

Regret Minimization ◽

Random Regret Minimization

In this study, discrete choice models that combine different behavioural rules are estimated to study the visitors’ preferences in relation to their travel mode choices to access a national park. Using a revealed preference survey conducted on visitors of Teide National Park (Tenerife, Spain), we present a hybrid model specification—with random parameters—in which we assume that some attributes are evaluated by the individuals under conventional random utility maximization (RUM) rules, whereas others are evaluated under random regret minimization (RRM) rules. We then compare the results obtained using exclusively a conventional RUM approach to those obtained using both RUM and RRM approaches, derive monetary valuations of the different components of travel time and calculate direct elasticity measures. Our results provide useful instruments to evaluate policies that promote the use of more sustainable modes of transport in natural sites. Such policies should be considered as priorities in many national parks, where negative transport externalities such as traffic congestion, pollution, noise and accidents are causing problems that jeopardize not only the sustainability of the sites, but also the quality of the visit.

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Traffic congestion and travel time prediction based on historical congestion maps and identification of consensual days

Transportation Research Part C Emerging Technologies ◽

10.1016/j.trc.2020.102920 ◽

2021 ◽

Vol 124 ◽

pp. 102920

Author(s):

Nicolas Chiabaut ◽

Rémi Faitout

Keyword(s):

Travel Time ◽

Traffic Congestion ◽

Travel Time Prediction ◽

Time Prediction

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Measuring Recurrent and Nonrecurrent Traffic Congestion

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1856-12 ◽

2003 ◽

Vol 1856 (1) ◽

pp. 118-124 ◽

Cited By ~ 84

Author(s):

Alexander Skabardonis ◽

Pravin Varaiya ◽

Karl F. Petty

Keyword(s):

Los Angeles ◽

Travel Time ◽

San Francisco ◽

Traffic Congestion ◽

Real Life ◽

Time Variability ◽

Loop Detectors ◽

Bay Area ◽

Freeway Corridors ◽

Travel Time Variability

A methodology and its application to measure total, recurrent, and nonrecurrent (incident related) delay on urban freeways are described. The methodology used data from loop detectors and calculated the average and the probability distribution of delays. Application of the methodology to two real-life freeway corridors in Los Angeles, California, and one in the San Francisco, California, Bay Area, indicated that reliable measurement of congestion also should provide measures of uncertainty in congestion. In the three applications, incident-related delay was found to be 13% to 30% of the total congestion delay during peak periods. The methodology also quantified the congestion impacts on travel time and travel time variability.

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Road Traffic Congestion Management Based on a Search-Allocation Approach

Transport and Telecommunication Journal ◽

10.1515/ttj-2017-0003 ◽

2017 ◽

Vol 18 (1) ◽

pp. 25-33 ◽

Cited By ~ 4

Author(s):

Jamal Raiyn

Keyword(s):

Travel Time ◽

Real Time ◽

Traffic Congestion ◽

Traffic Management ◽

Road Traffic ◽

Smart Cities ◽

Self Regulation ◽

Congestion Management ◽

Shortest Route ◽

Road Traffic Congestion

Abstract This paper introduces a new scheme for road traffic management in smart cities, aimed at reducing road traffic congestion. The scheme is based on a combination of searching, updating, and allocation techniques (SUA). An SUA approach is proposed to reduce the processing time for forecasting the conditions of all road sections in real-time, which is typically considerable and complex. It searches for the shortest route based on historical observations, then computes travel time forecasts based on vehicular location in real-time. Using updated information, which includes travel time forecasts and accident forecasts, the vehicle is allocated the appropriate section. The novelty of the SUA scheme lies in its updating of vehicles in every time to reduce traffic congestion. Furthermore, the SUA approach supports autonomy and management by self-regulation, which recommends its use in smart cities that support internet of things (IoT) technologies.

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The Morning Commute Problem with Ridesharing When Meet Stochastic Bottleneck

Sustainability ◽

10.3390/su13116040 ◽

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.

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