Modeling driving decisions with latent plans

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2007.Includes bibliographical references (p. 227-238).Driving is a complex task that includes a series of interdependent decisions. In many situations, these decisions are based on a specific plan. The plan is however unobserved or latent and only the manifestations of the plan through actions are observed. Examples include selection of a target lane before execution of the lane change, choice of a merging tactic before execution of the merge. Change in circumstances (e.g. reaction of the neighboring drivers, delay in execution) can lead to updates to the initially chosen plan. These latent plans are ignored in the state-of-the-art driving behavior models. Use of these myopic models in the traffic simulators often lead to unrealistic traffic flow characteristics and incorrect representation of congestion. A modeling methodology has been formulated to address the effects of unobserved plans in the decisions of the drivers and hence overcome the deficiency of the existing driving behavior models and simulation tools. The actions of the driver are conditional on the current plan. The current plan can depend on previous plans and be influenced by anticipated future conditions. A Hidden Markov Model is used to address the effect of previous plans in the choice of the current plan and to capture the state-dependence among decisions. Effects of anticipated future circumstances in the current plan are captured through predicted conditions based on current information. The heterogeneity in decision making and planning capabilities of drivers are explicitly addressed. The methodology has been applied in developing driving behavior models for four traffic scenarios: freeway lane changing, freeway merging, urban intersection lane choice and urban arterial lane changing. In all applications, the models are estimated with disaggregate trajectory data using the maximum likelihood technique.(cont.) Estimation results show that the latent plan models have a significantly better goodness-of-fit compared to the 'reduced form' models where the latent plans are ignored and only the choice of actions are modeled. The justifications for using the latent plan modeling approach are further strengthened by validation case studies within the microscopic traffic simulator MITSIMLab where the simulation capabilities of the latent plan models are compared against the reduced form models. In all cases, the latent plan models better replicate the observed traffic conditions.by Charisma Farheen Choudhury.Ph.D

Modeling lane-changing behavior in presence of exclusive lanes

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2005.Includes bibliographical references (p. 119-122).Driving behavior is significantly affected by the presence of exclusive lanes. Particularly, unlimited access to exclusive lanes result significant amount of special type of lane-changing actions. The objective of this thesis is to develop an improved lane-changing model that has a generalized structure and is flexible enough to capture the lane-changing behavior in all situations including the presence of unlimited access exclusive lanes. A new lane-changing model with explicit choice of target lane is proposed in this regard. The target lane is the lane the driver perceives as the best to be in taking a wide range of factors and goals into account. The direction of the immediate lane change is based on the choice of this target lane rather than myopic evaluation of adjacent lanes. A lane change occurs in the direction implied by the chosen target lane depending upon gap availability. The parameters of the model are jointly estimated with detailed vehicle trajectory data and calibrated for a situation with unlimited access High Occupancy Vehicle (HOV) lane. Estimation results show that the target lane choice is affected by lane-specific attributes, such as average speed and density, variables that relate to the path plan and the interactions of the vehicle with other vehicles surrounding it. The model is validated and compared with an existing lane-changing model using a microscopic traffic simulator in an HOV lane situation. The results indicate that the proposed model is significantly better than the previous model.by Charisma Farheen Choudhury.S.M

Development of origin–destination matrices using mobile phone call data

In this research, we propose a methodology to develop OD matrices using mobile phone Call Detail Records (CDR) and limited traffic counts. CDR, which consist of time stamped tower locations with caller IDs, are analyzed first and trips occurring within certain time windows are used to generate tower-to-tower transient OD matrices for different time periods. These are then associated with corresponding nodes of the traffic network and converted to node-to-node transient OD matrices. The actual OD matrices are derived by scaling up these node-to-node transient OD matrices. An optimization based approach, in conjunction with a microscopic traffic simulation platform, is used to determine the scaling factors that result best matches with the observed traffic counts. The methodology is demonstrated using CDR from 2.87 million users of Dhaka, Bangladesh over a month and traffic counts from 13 key locations over 3 days of that month. The applicability of the methodology is supported by a validation study

Multi-stage deep learning approaches to predict boarding behaviour of bus passengers

Smart card data has emerged in recent years and provide a comprehensive, and cheap source of information for planning and managing public transport systems. This paper presents a multi-stage machine learning framework to predict passengers’ boarding stops using smart card data. The framework addresses the challenges arising from the imbalanced nature of the data (e.g. many non-travelling data) and the ‘many-class’ issues (e.g. many possible boarding stops) by decomposing the prediction of hourly ridership into three stages: whether to travel or not in that one-hour time slot, which bus line to use, and at which stop to board. A simple neural network architecture, fully connected networks (FCN), and two deep learning architectures, recurrent neural networks (RNN) and long short-term memory networks (LSTM) are implemented. The proposed approach is applied to a real-life bus network. We show that the data imbalance has a profound impact on the accuracy of prediction at individual level. At aggregated level, FCN is able to accurately predict the rideship at individual stops, it is poor at capturing the temporal distribution of ridership. RNN and LSTM are able to measure the temporal distribution but lack the ability to capture the spatial distribution through bus lines

Modelling Acceleration Decisions in Traffic Streams with Weak Lane Discipline: A Latent Leader Approach

Acceleration is an important driving manoeuvre that has been modelled for decades as a critical element of the microscopic traffic simulation tools. The state-of-the art acceleration models have however primarily focused on lane based traffic. In lane based traffic, every driver has a single distinct lead vehicle in the front and the acceleration of the driver is typically modelled as a function of the relative speed, position and/or type of the corresponding leader. On the contrary, in a traffic stream with weak lane discipline, the subject driver may have multiple vehicles in the front. The subject driver is therefore subjected to multiple sources of stimulus for acceleration and reacts to the stimulus from the governing leader. However, only the applied accelerations are observed in the trajectory data, and the governing leader is unobserved or latent. The state-of-the-art models therefore cannot be directly applied to traffic streams with weak lane discipline. This prompts the current research where we present a latent leader acceleration model. The model has two components: a random utility based dynamic class membership model (latent leader component) and a class-specific acceleration model (acceleration component). The parameters of the model have been calibrated using detailed trajectory data collected from Dhaka, Bangladesh. Results indicate that the probability of a given front vehicle of being the governing leader can depend on the type of the lead vehicle and the extent of lateral overlap with the subject driver. The estimation results are compared against a simpler acceleration model (where the leader is determined deterministically) and a significant improvement in the goodness-of-fit is observed. The proposed models, when implemented in microscopic traffic simulation tools, are expected to result more realistic representation of traffic streams with weak lane discipline

Transferability of Car-Following Models Between Driving Simulator and Field Traffic

During the past few decades, there have been two parallel streams of driving behavior research: models using trajectory data collected from the field (using video recordings, GPS, etc.) and models using data from driving simulators (in which the behavior of the drivers is recorded in controlled laboratory conditions). Although the former source of data is more realistic, it lacks information about the driver and is typically not suitable for testing effects of future vehicle technologies and traffic scenarios. In contrast, driving behavior models developed with driving simulator data may lack behavioral realism. However, no previous study has compared these two streams of mathematical models and investigated the transferability of the models developed with driving simulator data to real field conditions in a rigorous manner. The current study aimed to fill this research gap by investigating the transferability of two car-following models between a driving simulator and two comparable real-life traffic motorway scenarios, one from the United Kingdom and the other one from the United States. In this regard, stimulus–response–based car-following models were developed with three microscopic data sources: (a) experimental data collected from the University of Leeds driving simulator, (b) detailed trajectory data collected from UK Motorway 1, and (c) detailed trajectory data collected from Interstate 80 in California. The parameters of these car-following models were estimated by using the maximum likelihood estimation technique, and the transferability of the models was investigated by using statistical tests of parameter equivalence and transferability test statistics. Estimation results indicate transferability at the model level but not fully at the parameter level for both pairs of scenarios

New appraisal values of travel time saving and reliability in Great Britain

© 2017, The Author(s). This paper provides an overview of the study ‘Provision of market research for value of time savings and reliability’ undertaken by the Arup/ITS Leeds/Accent consortium for the UK Department for Transport (DfT). The paper summarises recommendations for revised national average values of in-vehicle travel time savings, reliability and time-related quality (e.g. crowding and congestion), which were developed using willingness-to-pay (WTP) methods, for a range of modes, and covering both business and non-work travel purposes. The paper examines variation in these values by characteristics of the traveller and trip, and offers insights into the uncertainties around the values, especially through the calculation of confidence intervals. With regards to non-work, our recommendations entail an increase of around 50% in values for commute, but a reduction of around 25% for other non-work—relative to previous DfT ‘WebTAG’ guidance. With regards to business, our recommendations are based on WTP, and thus represent a methodological shift away from the cost saving approach (CSA) traditionally used in WebTAG. These WTP-based business values show marked variation by distance; for trips of less than 20miles, values are around 75% lower than previous WebTAG values; for trips of around 100miles, WTP-based values are comparable to previous WebTAG; and for longer trips still, WTP-based values exceed those previously in WebTAG

Quantum probability: A new method for modelling travel behaviour

There has been an increasing effort to improve the behavioural realism of mathematical models of choice, resulting in efforts to move away from random utility maximisation (RUM) models. Some new insights have been generated with, for example, models based on random regret minimisation (RRM, μ-RRM). Notwithstanding work using for example Decision Field Theory (DFT), many of the alternatives to RUM tested on real-world data have however only looked at only modest departures from RUM, and differences in results have consequently been small. In the present study, we address this research gap again by investigating the applicability of models based on quantum theory. These models, which are substantially different from the state-of-the-art choice modelling techniques, emphasise the importance of contextual effects, state dependence, interferences and the impact of choice or question order. As a result, quantum probability models have had some success in better explaining several phenomena in cognitive psychology. In this paper, we consider how best to operationalise quantum probability into a choice model. Additionally, we test the quantum model frameworks on a best/worst route choice dataset and demonstrate that they find useful transformations to capture differences between the attributes important in a most favoured alternative compared to that of the least favoured alternative. Similar transformations can also be used to efficiently capture contextual effects in a dataset where the order of the attributes and alternatives are manipulated. Overall, it appears that models incorporating quantum concepts hold significant promise in improving the state-of-the-art travel choice modelling paradigm through their adaptability and efficient modelling of contextual changes