scholarly journals Inferring the Purposes of using Ride-Hailing Services through Data Fusion of Trip Trajectories, Secondary Travel Surveys, and Land Use Data

2021 ◽  
pp. 036119812110035
Author(s):  
Sanjana Hossain ◽  
Khandker Nurul Habib
Keyword(s):  
Land Use ◽  
Data Fusion ◽  
Traffic Congestion ◽  
Travel Demand ◽  
Choice Model ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Small Sample ◽  
Trip Purpose ◽  
Transit Ridership

This paper presents a data fusion methodology for inferring trip purposes from GPS trajectories of ride-hailing services in Toronto. The methodology has a discrete choice model at its core that predicts the most probable purpose distributions using only basic trip-related information such as approximate pick-up and drop-off locations, trip start times, and land use characteristics around the origins and destinations. The choice model is estimated using revealed trip purpose data from a small-sample travel survey augmented by land use information from an enhanced point of interest database and the census. The methodology is applied to the trajectories of commercial ride-hailing trips made in Toronto between September 2016 and September 2018. For the core choice model, multinomial, nested, and mixed multinomial logit models are compared. Validation of the inferred trip purposes using the trip purpose proportions from another independent survey (not used in choice model estimation) reveal that the multinomial logit model can infer ride-hailing trip purpose distribution with reasonable accuracy. The inferred purpose distribution explains the nature of ride-hailing trips and provides important context of travel demand generated by the services. The results indicate that although ride-hailing services are mostly used for discretionary activities, they also play important roles in daily commuter travel. A quarter of the total weekday ride-hailing trips were made for work- and school-related activities. With increasing ridership, these services may start influencing conventional travel modes and thereby adversely affect the level of traffic congestion and transit ridership in the city.

scholarly journals Urban Planning: a Game Theory Application for the Travel Demand Management

2017 ◽  
Author(s):  
Mark Koryagin
Keyword(s):  
Land Use ◽  
Public Transport ◽  
Traffic Congestion ◽  
Travel Demand ◽  
Choice Model ◽  
Demand Management ◽  
Search Time ◽  
Travel Demand Management ◽  
Public Transport System ◽  
Theory Application

Urban infrastructure in the developing nations is generating a great number of environmental problems. Therefore, the problem of land distribution among road networks, parking spaces and landscaped parks is to be researched. The passenger behavior depends on traffic congestion, parking search time, public transport frequency, parking fee, etc. The travel mode choice model is described by logit function.A city territory is subdivided into three districts, residential, central and industrial, each of them trying to develop and implement the optimal policy of land use. The district criterion includes residential travel times, congestion and impacts of the parks on the environment. Any district should solve the effective land use problem while the public transport system tries to find the optimal frequency.The travel time depends on road capacity and is described by Greenshields model. The influence of parking capacity upon the parking search time is described by the BPR formula.Participants’ solutions influence one another; therefore, the coalition-free game is constructed. The existence of Nash equilibrium is proved for districts, passengers and public transport. The numerical example shows the impacts of value of time (VOT), population density and parking fee rates on districts land use.

Pricing Competition Under Specific Discrete Choice Models

2020 ◽  
Vol 37 (02) ◽  
pp. 2050008
Author(s):  
Farhad Etebari
Keyword(s):  
Discrete Choice ◽  
Logit Model ◽  
Choice Model ◽  
Public Information ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Discrete Choice Models ◽  
Nested Logit ◽  
Nested Logit Model ◽  
Substitution Pattern

Recent developments of information technology have increased market’s competitive pressure and products’ prices turned to be paramount factor for customers’ choices. These challenges influence traditional revenue management models and force them to shift from quantity-based to price-based techniques and incorporate individuals’ decisions within optimization models during pricing process. Multinomial logit model is the simplest and most popular discrete choice model, which suffers from an independence of irrelevant alternatives limitation. Empirical results demonstrate inadequacy of this model for capturing choice probability in the itinerary share models. The nested logit model, which appeared a few years after the multinomial logit, incorporates more realistic substitution pattern by relaxing this limitation. In this paper, a model of game theory is developed for two firms which customers choose according to the nested logit model. It is assumed that the real-time inventory levels of all firms are public information and the existence of Nash equilibrium is demonstrated. The firms adapt their prices by market conditions in this competition. The numerical experiments indicate decreasing firm’s price level simultaneously with increasing correlation among alternatives’ utilities error terms in the nests.

A Mode Choice Model for a Public Transport Transfer Center in Istanbul

2011 ◽  
Vol 97-98 ◽  
pp. 606-610
Author(s):  
Huseyın Onur Tezcan ◽  
Fatih Yonar ◽  
Sabahat Topuz Kiremitci
Keyword(s):  
Public Transport ◽  
Logit Model ◽  
Choice Model ◽  
Mode Choice ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Questionnaire Data ◽  
Mode Choice Model

The aim of this study is to understand the reasons behind the mode choice preferences of passengers using a public transport transfer center. For this aim, a questionnaire data obtained at an interim transfer center in Istanbul is utilized. This interim center hosts stops for paratransit, bus and metro modes. A multinomial logit model of modal preferences is estimated and the coefficient results of this model are used to analyze and compare modes.

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 Public preference for sustainable land use policies – Empirical results from multinomial logit model analysis

2022 ◽  
Author(s):  
Abdishakur W. Diriye ◽  
Osman M. Jama ◽  
Jama Warsame Diriye ◽  
Abdulhakim M Abdi
Keyword(s):  
Land Use ◽  
Logit Model ◽  
Policy Instruments ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Land Use Policy ◽  
Status Quo ◽  
Sustainable Land Use ◽  
Cultural Worldviews ◽  
Land Use Policies

Public preferences for sustainable land use policy instruments and the motivations behind such preferences are important to make appropriate policies. Based on survey data (n = 309) from northeastern Somalia, we examined preferences for a set of land use policy instruments relative to no policy (i.e. the current status quo) and how cultural worldviews predict such preferences. We used a multinomial logit model to analyze the comparative evaluation of choices due to its interpretability and robustness to violations of normality. Overall, the results show that the respondents are likely to consent to all types of land use policy instruments relative to no policy and are more inclined to market-based and informational policy instruments. Specifically, preferences for regulatory policy instruments are positively associated with hierarchy and egalitarian worldviews and are negatively associated with fatalism and individualistic worldviews with only hierarchy and fatalism are significant. The market-based policy instrument is desirable to all cultural worldviews except fatalism, but only egalitarian and individual worldviews are significant. Preferences for informational policy instruments are positively associated with all cultural worldviews but only egalitarian worldviews showed a significant effect. Although there are some contradictions, these results are broadly consistent with the proposition of the cultural theory of risk. This study highlights that preferences for land use policies are heterogeneous with cultural worldviews mainly explaining the sources of this heterogeneity. It is evident that the respondents were willing to consent to land use policies relative to the status quo of no policy and indicates the need for concerted effort to reduce land degradation and deforestation in the country. We, therefore, recommend that policymakers incorporate the different ways that humans perceive and interpret social-environmental relations into policy decisions to achieve sustainable policy outcomes.

scholarly journals Comparison of Four Types of Artificial Neural Network and a Multinomial Logit Model for Travel Mode Choice Modeling

2018 ◽  
Vol 2672 (49) ◽  
pp. 101-112 ◽  
Author(s):  
Dongwoo Lee ◽  
Sybil Derrible ◽  
Francisco Camara Pereira
Keyword(s):  
Neural Networks ◽  
Logit Model ◽  
Travel Demand ◽  
Mode Choice ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Choice Modeling ◽  
Probabilistic Neural Networks ◽  
Ann Models ◽  
Artificial Neural

Discrete choice modeling is a fundamental part of travel demand forecasting. To date, this field has been dominated by parametric approaches (e.g., logit models), but non-parametric approaches such as artificial neural networks (ANNs) possess much potential since choice problems can be assimilated to pattern recognition problems. In particular, ANN models are easily applicable with their higher capability to identify nonlinear relationships between inputs and designated outputs to predict choice behaviors. This article investigates the capability of four types of ANN model and compares their prediction performance with a conventional multinomial logit model (MNL) for mode choice problems. The four ANNs are: backpropagation neural networks (BPNNs), radial basis function networks (RBFNs), probabilistic neural networks (PNNs), and clustered probabilistic neural networks (CPNNs). To compare the modeling techniques, we present the algorithmic differences of each ANN technique, and we assess their prediction accuracy with a 10-fold cross-validation method. Furthermore, we assess the contribution of explanatory variables by conducting sensitivity analyses on significant variables. The results show that ANN models outperform MNL, with prediction accuracies around 80% compared with 70% for MNL. Moreover, PNN performs best out of all ANNs, especially to predict underrepresented modes.

scholarly journals Modeling Departure Time Choice of Car Commuters in Dhaka, Bangladesh

2021 ◽  
pp. 036119812110398
Author(s):  
Khatun Zannat ◽  
Charisma Farheen Choudhury ◽  
Stephane Hess
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Logit Model ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Data Sources ◽  
Similar Data ◽  
Schedule Delay ◽  
Departure Time ◽  
Departure Time Choice

Dhaka, one of the fastest-growing megacities in the world, faces severe traffic congestion leading to a loss of 3.2 million business hours per day. While peak-spreading policies hold the promise to reduce the traffic congestion levels, the absence of comprehensive data sources makes it extremely challenging to develop econometric models of departure time choices for Dhaka. This motivates this paper, which develops advanced discrete choice models of departure time choice of car commuters using secondary data sources and quantifies how level-of-service attributes (e.g., travel time), socio-demographic characteristics (e.g., type of job, income, etc.), and situational constraints (e.g., schedule delay) affect their choices. The trip diary data of commuters making home-to-work and work-to-home trips by personal car/ride-hailing services (957 and 934 respectively) have been used in this regard. Given the discrepancy between the stated travel times and those extracted using the Google Directions API, a sub-model is developed first to derive more reliable estimates of travel time throughout the day. A mixed multinomial logit model and a simple multinomial logit model are developed for outbound and return trip, respectively, to capture the heterogeneity associated with different departure time choice of car commuters. Estimation results indicate that the choices are significantly affected by travel time, schedule delay, and socio-demographic factors. The influence of type of job on preferred departure time (PDT) has been estimated using two different distributions of PDT for office employees and self-employed people (Johnson’s SB distribution and truncated normal respectively). The proposed framework could be useful in other developing countries with similar data issues.

Assortment Optimization and Pricing Under the Multinomial Logit Model with Impatient Customers: Sequential Recommendation and Selection

2021 ◽  
Author(s):  
Pin Gao ◽  
Yuhang Ma ◽  
Ningyuan Chen ◽  
Guillermo Gallego ◽  
Anran Li ◽  
...  
Keyword(s):  
Logit Model ◽  
Choice Model ◽  
Multinomial Logit Model ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Multinomial Logit ◽  
Impatient Customers ◽  
Purchasing Decisions ◽  
Assortment Optimization ◽  
Decision Variables

Sequential Recommendation Under the Multinomial Logit Model with Impatient Customers In many applications, customers incrementally view a subset of offered products and make purchasing decisions before observing all the offered products. In this case, the decision faced by a firm is not only what assortment of products to offer, but also in what sequence to offer the products. In “Assortment Optimization and Pricing Under the Multinomial Logit Model with Impatient Customers: Sequential Recommendation and Selection”, Gao, Ma, Chen, Gallego, Li, Rusmevichientong, and Topaloglu propose a choice model where each customer incrementally view the assortment of products in multiple stages, and their patience level determines the maximum number of stages. Under this choice model, the authors develop a polynomial-time algorithm that finds a revenue-maximizing sequence of assortments. If the sequence of assortments is fixed, the problem of finding revenue-maximizing prices can be transformed to a convex program. They combine these results to develop an effective approximation algorithm when both the sequence of assortments and prices are decision variables.

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