scholarly journals Analysis of Time of Day Fare Discounts on Urban Mass Transit Travel Behavior, Crowding, and Waiting Time

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Xiao Guo ◽  
Huijun Sun
Keyword(s):  
Objective Function ◽  
Waiting Time ◽  
Travel Behavior ◽  
Travel Demand ◽  
Mass Transit ◽  
Time Of Day ◽  
Pressure Equalization ◽  
Schedule Delay ◽  
Urban Mass Transit ◽  
Artificial Network

Every morning, commuters select the regularly dispatched urban mass transit for traveling from a residential area to a workplace. This paper aims to find an optimal discount fare and time intervals on morning peak hour. As a direct and flexible traffic economic instrument, fares can influence commuters’ behavior. Therefore, fare discount has been proposed to regulate traffic flow in different time. Two models have been analyzed to describe it with schedule delay because of the travel demand size. The first objective function is constructed on pressure equalization when the travel demand is small. The other objective function is to minimize total waiting time when the travel demand is large. In the end, numerical examples based on an artificial network are performed to characterize fare discount models.

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.

scholarly journals Cooperative Passenger Inflow Control in Urban Mass Transit Network with Constraint on Capacity of Station

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Jianyuan Guo ◽  
Limin Jia ◽  
Yong Qin ◽  
Huijuan Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Waiting Time ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Mass Transit ◽  
Constrained Optimization Problem ◽  
Swarm Optimization ◽  
Transit Network ◽  
Urban Mass Transit ◽  
Transfer Stations

In urban mass transit network, when passengers’ trip demands exceed capacity of transport, the numbers of passengers accumulating in the original or transfer stations always exceed the safety limitation of those stations. It is necessary to control passenger inflow of stations to assure the safety of stations and the efficiency of passengers. We define time of delay (TD) to evaluate inflow control solutions, which is the sum of waiting time outside of stations caused by inflow control and extra waiting time on platform waiting for next coming train because of insufficient capacity of first coming train. We build a model about cooperative passenger inflow control in the whole network (CPICN) with constraint on capacity of station. The objective of CPICN is to minimize the average time of delay (ATD) and maximum time of delay (MTD). Particle swarm optimization for constrained optimization problem is used to find the optimal solution. The numeral experiments are carried out to prove the feasibility and efficiency of the model proposed in this paper.

Temporal-Spatial Microassignment and Sequencing of Travel Demand with Activity-Trip Chains

2003 ◽  
Vol 1831 (1) ◽  
pp. 89-97 ◽  
Author(s):  
Ahmed F. Abdelghany ◽  
Hani S. Mahmassani
Keyword(s):  
Travel Time ◽  
Travel Behavior ◽  
Travel Demand ◽  
User Equilibrium ◽  
Stochastic Dynamic ◽  
Arrival Times ◽  
Schedule Delay ◽  
Trip Chain ◽  
Trip Chaining ◽  
Distance Travel

A stochastic temporal–spatial microassignment and activity sequencing model for activity–trip chains is presented. In this model, trip-chain patterns are defined by the respective locations of destinations in the chain, preferred arrival times at these destinations, and the activity durations at the intermediate destinations; they are given as input to the model. A stochastic dynamic user equilibrium problem is formulated and solved for this purpose. In this problem, drivers simultaneously seek to determine their departure time, route choice, and sequence of their intermediate activities at the origin to minimize their perceived travel cost. This perceived cost is typically a function of the travel time and the schedule delay at the intermediate and final destinations. The model is presented through a study of the relative efficiency of carpooling and trip-chaining travel behavior in a network context. In that example, the performance of travelers who have the option to carpool and chain trips is compared with that of households with single-occupant and individual trip-based travel. Several measures of travel performance, including travel distance, travel time, and schedule delay, are considered for that comparison.

Travel Behavior Classification: An Approach with Social Network and Deep Learning

2018 ◽  
Vol 2672 (47) ◽  
pp. 68-80 ◽  
Author(s):  
Yu Cui ◽  
Qing He ◽  
Alireza Khani
Keyword(s):  
Deep Learning ◽  
Social Network ◽  
Travel Behavior ◽  
Travel Demand ◽  
Similarity Measures ◽  
Detection Algorithm ◽  
Time Of Day ◽  
Working People ◽  
Matrix Similarity ◽  
Activity Matrix

Uncovering human travel behavior is crucial for not only travel demand analysis but also ride-sharing opportunities. To group similar travelers, this paper develops a deep-learning-based approach to classify travelers’ behaviors given their trip characteristics, including time of day and day of week for trips, travel modes, previous trip purposes, personal demographics, and nearby place categories of trip ends. This study first examines the dataset of California Household Travel Survey (CHTS) between the years 2012 and 2013. After preprocessing and exploring the raw data, an activity matrix is constructed for each participant. The Jaccard similarity coefficient is employed to calculate matrix similarities between each pair of individuals. Moreover, given matrix similarity measures, a community social network is constructed for all participants. A community detection algorithm is further implemented to cluster travelers with similar travel behavior into the same groups. There are five clusters detected: non-working people with more shopping activities, non-working people with more recreation activities, normal commute working people, shorter working duration people, later working time people, and individuals needing to attend school. An image of activity map is built from each participant’s activity matrix. Finally, a deep learning approach with convolutional neural network is employed to classify travelers into corresponding groups according to their activity maps. The accuracy of classification reaches up to 97%. The proposed approach offers a new perspective for travel behavior analysis and traveler classification.

Coast-to-Coast Comparison of Time Use and Activity Patterns

2000 ◽  
Vol 1718 (1) ◽  
pp. 34-42 ◽  
Author(s):  
Sachin Gangrade ◽  
Krishnan Kasturirangan ◽  
Ram M. Pendyala
Keyword(s):  
San Francisco ◽  
Time Use ◽  
Travel Behavior ◽  
Travel Demand ◽  
Activity Patterns ◽  
Time Of Day ◽  
Demographic Characteristics ◽  
Use Patterns ◽  
The Individual ◽  
End Times

Activity-based travel analysis has been gaining increasing attention in travel demand research during the past decade. Activity and trip information collected at the person level aids in understanding the underlying behavioral patterns of individuals and the interactions among their activities and trips. Activity and time use patterns across geographical contexts are compared. Such a comparison could shed light on the differences and similarities in travel behavior that exist between areas. To accomplish this objective, activity, travel, and time use information derived from surveys conducted in the San Francisco Bay and Miami areas has been analyzed to identify differences in activity engagement patterns across different sample groups. In general, it was found that activity and time use patterns are comparable across the two areas as long as the commuting status and demographic characteristics of the individuals are controlled for. In addition, the time-of-day distributions of various events such as wake-up time, sleeping time, time of departure and arrival at home, and work start and end times were compared. These events were considered important in defining the temporal constraints under which people exercise activity and travel choices. Once again, it was found that the distributions followed similar trends as long as the commuting status and the demographic characteristics of the individual were controlled for. However, there were noticeable differences that merit further investigation.

Study on Connection Optimization of Last Train Departure Time on Urban Mass Transit Network

2014 ◽  
Vol 1030-1032 ◽  
pp. 2211-2214 ◽  
Author(s):  
Chun Ge Kou ◽  
Shi Wei He ◽  
Bi Sheng He
Keyword(s):  
Numerical Analysis ◽  
Waiting Time ◽  
Mass Transit ◽  
Volume Distribution ◽  
Distribution Method ◽  
Transit Network ◽  
Departure Time ◽  
Urban Mass Transit ◽  
Passenger Volume ◽  
Operation And Management

The operation and management of urban mass transit network put forward higher requirements for last trains‘ transfer connection. Based on the analysis of coordination relationship and timeliness of accessible routes, this paper puts forward a dynamic passenger volume distribution method according to the generalized travel cost. Then the connection optimization model of last train departure time is built to increase accessible passenger volume and reduce passengers’ transfer waiting time of all OD pairs for last trains. Finally, the validity and rationality of this model and algorithm is verified with numerical analysis.

Analyzing Cell Phone Location Data for Urban Travel

2015 ◽  
Vol 2526 (1) ◽  
pp. 126-135 ◽  
Author(s):  
Serdar Çolak ◽  
Lauren P. Alexander ◽  
Bernardo G. Alvim ◽  
Shomik R. Mehndiratta ◽  
Marta C. González
Keyword(s):  
Travel Behavior ◽  
Rio De Janeiro ◽  
Cell Phone ◽  
Travel Demand ◽  
Census Data ◽  
Low Cost ◽  
Time Of Day ◽  
Raw Data ◽  
Home Based ◽  
Demand Models

Travelers today use technology that generates vast amounts of data at low cost. These data could supplement most outputs of regional travel demand models. New analysis tools could change how data and modeling are used in the assessment of travel demand. Recent work has shown how processed origin–destination trips, as developed by trip data providers, support travel analysis. Much less has been reported on how raw data from telecommunication providers can be processed to support such an analysis or to what extent the raw data can be treated to extract travel behavior. This paper discusses how cell phone data can be processed to inform a four-step transportation model, with a focus on the limitations and opportunities of such data. The illustrated data treatment approach uses only phone data and population density to generate trip matrices in two metropolitan areas: Boston, Massachusetts, and Rio de Janeiro, Brazil. How to label zones as home- and work-based according to frequency and time of day is detailed. By using the labels (home, work, or other) of consecutive stays, one can assign purposes to trips such as home-based work. The resulting trip pairs are expanded for the total population from census data. Comparable results with existing information reported in local surveys in Boston and existing origin–destination matrices in Rio de Janeiro are shown. The results detail a method for use of passively generated cellular data as a low-cost option for transportation planning.

MODEL DISTRIBUSI PERJALANAN PENDUDUK UNTUK PENENTUAN KORIDOR ‘LRT’ DI PULAU BATAM

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Djoko Prijo Utomo
Keyword(s):  
Network Performance ◽  
Travel Demand ◽  
Mass Transit ◽  
Distribution Model ◽  
Economic Activities ◽  
Transit System ◽  
Trip Distribution ◽  
Home Interview ◽  
Interview Survey ◽  
Private Car

In consequence of the increasing of regional economic activities in Pulau Batam, a reliable transportation system is required. Decreasing road network performance as a result of increasing traffic volume needs a strategic planning to anticipate the worsening condition in the future. One of the solutions is by providing mass transit system which is expected to attract private car users. Therefore, determination of potential corridor of mass transit system need to be identified so that the system provide better accessibility. Trip pattern in Pulau Batam must be known by developing trip distribution model. The trip distribution model is calibrated using origin-destination (O-D) data that is based on home interview survey. The validated model will be used to forecast and simulate travel demand onto transport network. Result of model calibration process shows mean trip length difference between model and survey is equal 0.141 %. From simulation of trip assignment is obtained that potential corridor for mass transit system using LRT is Batu Ampar – Batu Aji via Muka Kuning. Passenger forecast in the year 2030 is 193,990 passenger/day (2 directions).

Using Deep Learning to Understand Travel Demands in Different Urban Districts

2021 ◽  
pp. 036119812199458
Author(s):  
Shunhua Bai ◽  
Junfeng Jiao
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Prediction Accuracy ◽  
Travel Demand ◽  
Short Term Memory ◽  
Future Research ◽  
Demand Forecast ◽  
Time And Space ◽  
The University ◽  
The Impact

Travel demand forecast plays an important role in transportation planning. Classic models often predict people’s travel behavior based on the physical built environment in a linear fashion. Many scholars have tried to understand built environments’ predictive power on people’s travel behavior using big-data methods. However, few empirical studies have discussed how the impact might vary across time and space. To fill this research gap, this study used 2019 anonymous smartphone GPS data and built a long short-term memory (LSTM) recurrent neural network (RNN) to predict the daily travel demand to six destinations in Austin, Texas: downtown, the university, the airport, an inner-ring point-of-interest (POI) cluster, a suburban POI cluster, and an urban-fringe POI cluster. By comparing the prediction results, we found that: the model underestimated the traffic surge for the university in the fall semester and overestimated the demand for downtown on non-working days; the prediction accuracy for POI clusters was negatively related to their adjacency to downtown; and different POI clusters had cases of under- or overestimation on different occasions. This study reveals that the impact of destination attributes on people’s travel demand can vary across time and space because of their heterogeneous nature. Future research on travel behavior and built environment modeling should incorporate the temporal inconsistency to achieve better prediction accuracy.

Sign in / Sign up

Export Citation Format

Share Document