scholarly journals Myopic choice or rational decision making? An investigation into mode choice preference structures in competitive modal arrangements in a multimodal urban area, the City of Toronto

2016 ◽  
Vol 43 (5) ◽  
pp. 420-428 ◽  
Author(s):  
Mohamed Salah Mahmoud ◽  
Adam Weiss ◽  
Khandker Nurul Habib
Keyword(s):  
Urban Areas ◽  
Choice Model ◽  
Mode Choice ◽  
Model Development ◽  
Choice Preference ◽  
Proposed Model ◽  
Mode Share ◽  
Household Travel ◽  
The City ◽  
Dependent Behaviour

This paper presents an investigation into the preference structure of commuting mode choice in dense urban areas. The paper aims to investigate the phenomenon of myopic choice and extends the phenomenon to the concept of modal culture. Using a household travel diary survey from the greater Toronto and Hamilton Area, an empirical discrete choice model was estimated. This model was used to provide general comments on the commuting and dependent behaviour of the sample, with a particular focus on the factors that influence bicycling captivation and culture. The model was then used for a hypothetical policy scenario analysis, which found that an investment in biking infrastructure had the capacity to increase bicycling mode share by nearly 50%. Based on this result, this paper recommends further investigation into both data collection for more comprehensive empirical model development and investigation into the policy applicability of the proposed model structure.

A Heteroscedastic Polarized Logit Model to Investigate the Competition of Bicycle for the Bikeable Trips with the Other Modes

2018 ◽  
Vol 2672 (49) ◽  
pp. 58-68
Author(s):  
Khandker Nurul Habib
Keyword(s):  
Choice Model ◽  
National Capital Region ◽  
Travel Mode ◽  
Household Travel Survey ◽  
Market Shares ◽  
Proposed Model ◽  
National Capital ◽  
Mode Share ◽  
Household Travel ◽  
Low Sensitivity

The paper proposes a new discrete choice model, named the Heteroscedastic Polarized Logit (HPL) to investigate choice contexts with one or more alternatives with remarkably low market shares. The proposed model is used to investigate the factors influencing the choice of a bicycle as a travel mode in the National Capital Region (NCR) of Canada. Data from the latest household travel survey of the NCR are used to investigate the mode choices of bikeable trips. Bikeable trips are defined as trips with lengths shorter than 16 km as this is the observed maximum limit of a bicycle trip in the dataset. A large dataset with over 40,000 trip records is used for empirical investigation where the bicycle has the lowest mode share of 3%. The HPL model clearly shows its appropriateness and superiority over comparable models in such a context. The choice to walk is found to be more sensitive to trip length than the choice to cycle, yet walking is found to have three times larger market share than that of cycling. Similarly, motorized modes are found to have low sensitivity to travel time and other impedances and have larger market shares. Women and students are found not to prefer the bicycle as a travel mode. Cycling infrastructure is seen to be effective in increasing the choice of the bicycle as a travel mode, but it also becomes clear that additional soft policy initiatives would be necessary to increase the popularity of cycling among young people, students, and women.

scholarly journals The Design of Sustainable City Multi-Floor Manufacturing Processes Under Uncertainty in Supply Chains

2020 ◽  
Vol 12 (22) ◽  
pp. 9439
Author(s):  
Tygran Dzhuguryan ◽  
Agnieszka Deja ◽  
Bogusz Wiśnicki ◽  
Zofia Jóźwiak
Keyword(s):  
Supply Chains ◽  
Urban Areas ◽  
Flow Analysis ◽  
Material Flow Analysis ◽  
Performance Model ◽  
Sustainable City ◽  
Sustainable Supply Chains ◽  
Capacity Loss ◽  
Proposed Model ◽  
The City

The application of multi-floor manufacturing (MFM) in huge cities is related to the rational use of urban areas and the solution to traffic problems. The operation of the city MFM clusters depends on the efficiency of production and transport management considering technical, economic, environmental, and other factors. The primary goal of this paper was to identify and analyze the drivers of sustainable supply chains (SSCs) that influence or encourage the design of sustainable processes in city MFM clusters under uncertainty in supply chains. This paper presents an SSC performance model for city MFM clusters under uncertainty. The proposed model is universal and is based on material flow analysis (MFA) methodology. The presented analysis helps to determine the conditions for rhythmic deliveries with the use of the multi-IRTs. The coefficients of rhythmic deliveries for multiple intelligent reconfigurable trolleys (IRTs) and the capacity loss of freight elevators allow us to periodically assess the sustainability processes in city MFM clusters related to the flow materials. These assessments are the basis for the decision-making and planning of SSCs.

Forecasting Paratransit Ridership Using Discrete Choice Models with Explicit Consideration of Availability

1998 ◽  
Vol 1618 (1) ◽  
pp. 60-65 ◽  
Author(s):  
Julian Benjamin ◽  
Shinya Kurauchi ◽  
Takayuki Morikawa ◽  
Amalia Polydoropoulou ◽  
Kuniaki Sasaki ◽  
...  
Keyword(s):  
Choice Model ◽  
Mode Choice ◽  
Cost Effective ◽  
The Elderly ◽  
Developed Countries ◽  
Discrete Choice Models ◽  
Mode Share ◽  
Bus Service ◽  
Reservation System ◽  
The Impact

In most developed countries, the population of the elderly and disabled is growing rapidly. These individuals require transportation service suited to their needs. Such service may be provided by applying emerging technologies to dial-a-ride transit. This research develops a methodology to quantitatively evaluate the impact of paratransit services on a traveler’s mode choice behavior. The mode choice model explicitly considers availability of alternative modes and includes latent factors to account for taste heterogeneity. Stated preferences are also used to elicit preferences for new paratransit services. The methodology is empirically tested with data collected in Winston-Salem, North Carolina. The model system developed is applied to evaluate the effect of improving service attributes and the impact of the introduction of new cost-effective modes on modal shares. Results of the policy analysis indicate that ( a) transit policy changes, such as fare reduction, would have little effect on automobile driver and automobile passenger shares; ( b) an improved reservation system for dial-a-ride services would produce shifts in mode share; ( c) the proposed new bus deviation service was favored; ( d) free bus service reduces dial-a-ride share; and ( e) an increase in awareness of a dial-a-ride system would significantly increase its share.

scholarly journals A Bayesian Network Model on the Public Bicycle Choice Behavior of Residents: A Case Study of Xi’an

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Qiuping Wang ◽  
Hao Sun ◽  
Qi Zhang
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Expert Knowledge ◽  
Choice Model ◽  
Mode Choice ◽  
Bayesian Network Model ◽  
Travel Mode ◽  
The Public ◽  
Proposed Model ◽  
Travel Mode Choice

In order to study the main factors affecting the behaviors that city residents make regarding public bicycle choice and to further study the public bicycle user’s personal characteristics and travel characteristics, a travel mode choice model based on a Bayesian network was established. Taking residents of Xi’an as the research object, a K2 algorithm combined with mutual information and expert knowledge was proposed for Bayesian network structure learning. The Bayesian estimation method was used to estimate the parameters of the network, and a Bayesian network model was established to reflect the interactions among the public bicycle choice behaviors along with other major factors. The K-fold cross-validation method was used to validate the model performance, and the hit rate of each travel mode was more than 80%, indicating the precision of the proposed model. Experimental results also present the higher classification accuracy of the proposed model. Therefore, it may be concluded that the resident travel mode choice may be accurately predicted according to the Bayesian network model proposed in our study. Additionally, this model may be employed to analyze and discuss changes in the resident public bicycle choice and to note that they may possibly be influenced by different travelers’ characteristics and trip characteristics.

Mode Choice Behavior Analysis by Using a Semi-Compensatory Discrete Choice Model

2013 ◽  
Vol 838-841 ◽  
pp. 3300-3304
Author(s):  
Chong Wei ◽  
Lin Xiao ◽  
Chun Fu Shao
Keyword(s):  
Discrete Choice ◽  
Posterior Distribution ◽  
Choice Behavior ◽  
Data Augmentation ◽  
Choice Model ◽  
Mode Choice ◽  
Estimation Procedure ◽  
Bayes Theorem ◽  
Proposed Model ◽  
Conjunctive Rule

In this study we proposed a semi-compensatory model to analyze the mode choice behavior. The proposed model formulated the conjunctive rule through a straightforward way. The proposed model can take into account the probability distribution of the threshold involved by the conjunctive rule. To estimate the parameters of the proposed model, we derived the posterior distribution of the parameters by using the Bayes theorem and developed a blacked Metropolis-Hastings algorithm to carry out the estimation based on the posterior distribution. We also employed the data augmentation technology to simplify the estimation procedure. The proposed model was validated by using a SP survey dataset. We compared the performance of the proposed model to that of the logit model.

scholarly journals Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data

2021 ◽  
Vol 10 (3) ◽  
pp. 165
Author(s):  
Joerg Schweizer ◽  
Cristian Poliziani ◽  
Federico Rupi ◽  
Davide Morgano ◽  
Mattia Magi
Keyword(s):  
Big Data ◽  
Urban Areas ◽  
Autonomous Vehicles ◽  
Large Scale ◽  
Choice Model ◽  
Demand Model ◽  
Agent Based ◽  
Traffic Lights ◽  
Gps Traces ◽  
The City

A large-scale agent-based microsimulation scenario including the transport modes car, bus, bicycle, scooter, and pedestrian, is built and validated for the city of Bologna (Italy) during the morning peak hour. Large-scale microsimulations enable the evaluation of city-wide effects of novel and complex transport technologies and services, such as intelligent traffic lights or shared autonomous vehicles. Large-scale microsimulations can be seen as an interdisciplinary project where transport planners and technology developers can work together on the same scenario; big data from OpenStreetMap, traffic surveys, GPS traces, traffic counts and transit details are merged into a unique transport scenario. The employed activity-based demand model is able to simulate and evaluate door-to-door trip times while testing different mobility strategies. Indeed, a utility-based mode choice model is calibrated that matches the official modal split. The scenario is implemented and analyzed with the software SUMOPy/SUMO which is an open source software, available on GitHub. The simulated traffic flows are compared with flows from traffic counters using different indicators. The determination coefficient has been 0.7 for larger roads (width greater than seven meters). The present work shows that it is possible to build realistic microsimulation scenarios for larger urban areas. A higher precision of the results could be achieved by using more coherent data and by merging different data sources.

scholarly journals Integrated Evaluation Model of Construction Vulnerability in Anthropic Fill Areas (IALV). Case Study: District of Villaverde in the City of Madrid, Spain

2021 ◽  
Vol 13 (15) ◽  
pp. 8575
Author(s):  
Félix Escolano Sánchez ◽  
Francisco Parra Idreos ◽  
Manuel Bueno Aguado
Keyword(s):  
Urban Areas ◽  
Evaluation Model ◽  
Vulnerability Index ◽  
Soil Conditions ◽  
Review Of Literature ◽  
Proposed Model ◽  
The World ◽  
The City ◽  
Study District

Over the coming years, developments of large urban areas are expected, many of them on plots where soil conditions may not be the most suitable for building. This is the case of plots that previously have been used for dumping anthropic fill deposits. The term anthropic fill included a large variety of materials, all of them related with human activity; but this paper is mainly focused on natural materials extracted from nearby excavations or construction debris that form non-contaminated lands. In a review of literature related to risks, it is observed that in the last 10 years there have been abundant investigations to determine vulnerability in urban areas. However, the risks derived from the presence of anthropic landfills have generally been overlooked. For this reason, there is a real need to quantify construction vulnerability in areas settled on anthropic landfills. A methodology, up to now unknown, must be created to estimate and extrapolate it to any part of the world. The aim is to avoid the likelihood of pathologies appearing in urban areas. Hence, and to address this lack of knowledge, an Integrated Evaluation Model has been developed. Its purpose is to quantify, simply but effectively, the construction vulnerability index in already consolidated areas of historic landfills. The proposed model has been validated in a very popular district of the city of Madrid. Its surface, the number of buildings affected and population involved make it truly representative.

scholarly journals Choice Modelling of a Car Traveler towards Park-and-Ride Services in Putrajaya to Create Green Development

2021 ◽  
Vol 13 (14) ◽  
pp. 7869
Author(s):  
Irfan Ahmed Memon ◽  
Noman Sahito ◽  
Saima Kalwar ◽  
Jinsoo Hwang ◽  
Madzlan Napiah ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Choice Model ◽  
Low Cost ◽  
Mode Choice ◽  
City Centre ◽  
Choice Modelling ◽  
Park And Ride ◽  
Green Development ◽  
Mode Of Transport ◽  
The City

Putrajaya is facing an increasing number of private car ownership and its usage. Integrated transportation infrastructure connecting the city with suburban areas and comparatively low-cost housing schemes are at the fringes of Putrajaya City. It creates a discrepancy between housing and employment attentiveness. Due to the attractiveness of jobs in the city centre, commuters’ travelling pattern is morning/evening peak hours, and it leads to traffic congestion on a few major artilleries leading to and from the city. In contrast, Putrajaya was designed to achieve a 70:30 modal split ratio. This policy was introduced to target 70% of the commuters towards a sustainable mode of transport as their mode choice. Currently, congestion in Putrajaya is due to the use of single-occupant vehicles (SOV). The SOV users cannot be convinced to use the park-and-ride services (P&RS) without understanding their travel behaviors. Therefore, the mode choice models (MCM) were developed through binary logit regression (BLR) approaches to determine the factors that influence the SOV travelers’ decisions to adopt the P&RS. As a result, several factors, which included the socio-demographic factors, travel time, travel expenses, environmental protection, avoiding stress, parking problems, vehicles sharing, and traveling directly, were found to be significant and will promote green development. Furthermore, the quality of the developed mode choice model was validated through the training and testing approach of logistic regression. Ultimately, this study can help stakeholders to encourage SOV users towards P&RS by overcoming these factors.

Application of Machine Learning to Two Large-Sample Household Travel Surveys: A Characterization of Travel Modes

2019 ◽  
Vol 2673 (4) ◽  
pp. 173-183
Author(s):  
Robert Chapleau ◽  
Philippe Gaudette ◽  
Tim Spurr
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithm ◽  
Choice Model ◽  
Mode Choice ◽  
Confusion Matrix ◽  
Random Forest Algorithm ◽  
Household Travel ◽  
Household Travel Surveys

Even in a context of rapidly evolving transportation and information technologies, household travel surveys remain an essential source of information for transportation planning. Moreover, as planning authorities become increasingly concerned with reducing the use of the private car, travelers’ mode choice patterns should be reexamined. In this study, a machine learning algorithm (Random Forest) was employed to characterize the use of eight different travel modes observed in two consecutive household travel surveys undertaken in Montreal, Canada. The analysis incorporated roughly 160,000 observed trips. The Random Forest algorithm was trained on the 2008 survey data and applied to the 2013 survey. The usefulness of the algorithm was evaluated using two numerical representations: the confusion matrix and the importance matrix. The results of this evaluation showed that the Random Forest algorithm could generate a detailed and precise characterization of travel submarkets for four of the most commonly observed modes of travel (auto-drive, public transit, school bus, and walk) using 11 attributes of households, persons, and trips. However, the auto-passenger mode was difficult to characterize because of its dependence on unobserved intra-household interactions. The algorithm also had difficulty identifying users of rarely observed modes (park-and-ride, kiss-and-ride, bicycle), but performed better in this regard than a traditional mode choice model. Finally, traveler’s age and the spatial orientation of origin–destination pairs were found to be decisive factors in the use of the auto-drive mode. This finding, combined with the stability of mode choice patterns observed over 5 years, highlights the difficulty of significantly reducing automobile use.

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