Exploring the Utility of Gini Coefficients as a Measure of Temporal Variation in Public Transit Travel Time

2021 ◽  
pp. 1-13
Author(s):  
Joe Chestnut ◽  
E. Eric Boschmann
Keyword(s):  
Temporal Variation ◽  
Travel Time ◽  
Public Transit ◽  
Gini Coefficients

scholarly journals Bus Travel Time Prediction: A Comparative Study of Linear and Non-Linear Machine Learning Models

2022 ◽  
Vol 2161 (1) ◽  
pp. 012053
Author(s):  
B P Ashwini ◽  
R Sumathi ◽  
H S Sudhira
Keyword(s):  
Random Forest ◽  
Travel Time ◽  
Linear Models ◽  
Public Transit ◽  
Regression Trees ◽  
Support Vector ◽  
Learning Models ◽  
Random Forest Regression ◽  
The Public ◽  
Non Linear

Abstract Congested roads are a global problem, and increased usage of private vehicles is one of the main reasons for congestion. Public transit modes of travel are a sustainable and eco-friendly alternative for private vehicle usage, but attracting commuters towards public transit mode is a mammoth task. Commuters expect the public transit service to be reliable, and to provide a reliable service it is necessary to fine-tune the transit operations and provide well-timed necessary information to commuters. In this context, the public transit travel time is predicted in Tumakuru, a tier-2 city of Karnataka, India. As this is one of the initial studies in the city, the performance comparison of eight Machines Learning models including four linear namely, Linear Regression, Ridge Regression, Least Absolute Shrinkage and Selection Operator Regression, and Support Vector Regression; and four non-linear models namely, k-Nearest Neighbors, Regression Trees, Random Forest Regression, and Gradient Boosting Regression Trees is conducted to identify a suitable model for travel time predictions. The data logs of one month (November 2020) of the Tumakuru city service, provided by Tumakuru Smart City Limited are used for the study. The time-of-the-day (trip start time), day-of-the-week, and direction of travel are used for the prediction. Travel time for both upstream and downstream are predicted, and the results are evaluated based on the performance metrics. The results suggest that the performance of non-linear models is superior to linear models for predicting travel times, and Random Forest Regression was found to be a better model as compared to other models.

scholarly journals Evaluating accessibility of small communities via public transit

2020 ◽  
Vol 56 (4) ◽  
pp. 59-72
Author(s):  
Antonio Danesi ◽  
Simone Tengattini
Keyword(s):  
Travel Time ◽  
Public Transport ◽  
Public Transit ◽  
Time Of Day ◽  
Educational Work ◽  
Small Communities ◽  
Schedule Delay ◽  
Total Travel Time ◽  
Travel Impedance ◽  
The City

Accessibility to and from urban centres allows small communities’ dwellers to participate in primary activities and use essential services that are not available on-site, such as educational, work and medical services. Public transport networks are supposed to enhance accessibility and pursue equity principles, overcoming socio-economical differences among people that can exacerbate during crisis. In this paper a methodology is proposed and implemented to assess small communities’ accessibility via public transit. A metric is defined based on the calculation of total travel time, taken as a proxy of travel impedance, with consideration of in-vehicle time, schedule delay and users’ arrival and departure preference curves (i.e. time-of-day functions). A “rooftops” model is specified and implemented under the assumption that travellers cannot accept (scheduled) late arrival or early departure time penalties before and after the participation in their activities in the main urban centre, as many activities rarely admit time-flexibility. Also, a public transport specific impedance factor (PTSIF) is proposed, in order to account for travel impedance determinants, which are a consequence of service scheduling and routing decisions and not due to inherent geographical and infrastructural disadvantages affecting car users too. An application of the methodology for the city of Cesena, Italy, and 90 surrounding small communities is presented. The city is served by train and bus services. Assessment of small communities' accessibility based on both total travel time and PTSIF is presented and discussed. This practice-ready quantitative method can help transport professionals to evaluate impacts on small communities’ accessibility in light of public transport service changes or reduction. Quantitative approach to support strategic decisions is needed, for example, both to assess public transport strengthening politics against depopulation of rural and marginal mountainous areas and to mitigate the effects of possible increasing concentration of services towards high-demand lines, which may follow as a consequence of budget cuts or contingencies, such as vehicle capacity reductions required by sanitary emergencies.

Public Transit Based Commuting Travel Time Impact Models

CICTP 2018 ◽  
2018 ◽  
Author(s):  
Zhe Liu ◽  
Jiancheng Weng ◽  
Qiang Tu ◽  
Ledian Zhang
Keyword(s):  
Travel Time ◽  
Public Transit ◽  
Impact Models

scholarly journals Identification of Factors to Improve Public Transit Services (A Case Study of Prithvi Chowk to Talchowk Section of Prithivi Highway)

2021 ◽  
Vol 11 (4) ◽  
Author(s):  
Saroj Baral ◽  
Prem Nath Bastola
Keyword(s):  
Travel Time ◽  
Public Transport ◽  
Public Transit ◽  
The Public ◽  
Public Transport System ◽  
Transit Services ◽  
Transport Carrier ◽  
Time Required ◽  
Causes Of Delays

This research presents studies on a segment of highway to determine the quantitative factors that inuence transit services. Travel time and delay study is one of the method to determine quantitative factors. Tour time is described as the average period of time required to journey from one region to some other. Total departure time consists of gadgets which include total working time, places and general delay time. The examine section was done in Prithvi chowk to Tal chowk of Prithvi Highway which is turned to be 12.5 km long. Additionally, it has been found that the principle variables affecting travel time are: postpone time because of forestall selecting and choosing up passengers, bus model and bus size.32 trips public transport carrier and a 10 trips non-public automobile journey have been held during peak hours. Models are developed the use of SPSS software to become aware of the relationship between the causes of delays and the overall-time delays. Travel time and learning delays can help reduce the number of private vehicles operating and increase the number of public vehicles in order to reduce congestion and improve the e efficiency of the public transport system. It turned into determined that there was a full-size distinction in tour time among the use of the public transit services and the car.

scholarly journals Time slows down on a crowded train

2021 ◽  
Author(s):  
Saeedeh Sadeghi ◽  
Ricardo Daziano ◽  
So-Yeon Yoon ◽  
Adam K. Anderson
Keyword(s):  
Heart Rate ◽  
Virtual Reality ◽  
Negative Affect ◽  
Travel Time ◽  
Social Life ◽  
Public Transit ◽  
Trip Duration ◽  
Negative Feelings

Numerosity, complexity and affect are among factors known to dilate perceived time. While such objective and subjective factors are usually tested in isolation with simple stimuli in the lab, here we examined the perceived passage of time in the ecology of daily social life: crowded public transit. Higher crowding level denotes a higher numerosity along with increased negative affect. Accordingly, we hypothesized that crowding lengthens subjective trip duration. Participants (N=41) experienced short (between 1 to 2 minutes) immersive subway trips using Virtual Reality (VR). Each individual experienced multiple virtual trips with different crowding levels. After each trip, they were asked to estimate the trip duration and rate its affective pleasantness. Presence of one additional person per square meter of the train significantly increased perceived travel time by an average of 1.8 seconds. Rather than objective factors, this effect was mediated by subjective negative feelings induced by crowding. Analysis of cardiac data also revealed the slope of change in heart rate during a trip as a physiological source of perceived travel time, independent of the crowding level. This study is an example of bringing basic psychological and physiological findings into an ecologically valid setting using VR technology. Findings have broader implications for the effects of disliking social crowding on our daily perceptions, which is likely more pronounced during or even after the COVID-19 pandemic.

Sign in / Sign up

Export Citation Format

Share Document