- Home
- Research
- Core Topics
- Transportation Operations
Transportation Operations
Research
Characterizing visitor engagement behavior at large-scale events: Activity sequence clustering and ranking using GPS tracking data
Year: 2022
This study uses GPS data of 1461 participants at a planned special event organized in Oshkosh, Wisconsin named AirVenture to characterize their spatio-temporal activity participation behavior. The GPS data is used to derive activity sequences for participants and study the attractiveness of various activities at the event site. A validation procedure is proposed using aerial photos, from which crowd density is estimated and compared to heatmaps of GPS data. A machine learning clustering approach is used to group participants into market segments on the basis of their activity sequences. The results show a prevalence of 6 behavioral groups with statistical tests confirming significant differences related to movement and time use. Finally, a multinomial logit model is formulated, demonstrating that age, prior visitation, and attendance plan (daily vs. weekly) affect the typological behavior. The results reveal valuable insights that can help special event organizers with related marketing and planning strategies.
Read the full publication
Simulating Large-Scale Events as a Network of Heterogeneous Queues: Framework and Application
Researcher(s): Christopher Cummings, Hoseb Abkarian, Yuhan Zhou, Divyakant Tahlyan, Karen Smilowitz, Hani Mahmassani
Year: 2021
Large-scale planned special events (PSEs) can pose unique transportation and logistics challenges. Data collection and simulation are important tools to address these challenges, although they are often difficult because of event size and complexity. This paper discusses methods to address the challenge of multimodal simulation at large PSEs through the context of AirVenture, a large week-long airshow organized by the Experimental Aircraft Association in Oshkosh, Wisconsin. Sampling and data collection techniques are discussed for a variety of modal processes like private vehicles, pedestrians, and shuttles, and for different situations like vehicle arrivals and departures, pedestrian queues, and shuttle systems. A flexible simulation framework for integrating these three modes and numerous activities is developed as a network of heterogeneous queues and queue-dependent choices. The simulation tested a variety of proposed policy changes around the site, including rerouting shuttle lines, and adjusting the system of vehicle arrivals to the site. Results of this study demonstrate the effectiveness and flexibility of the data collection and simulation methodologies. The techniques developed in this work can be used to improve planning and transportation systems at many other forms of PSE.
Contextual trustworthiness of organizational partners: Evidence from nine school networks
Year: 2021
Problem definition: Trustworthy partners in procurement and service relationships are an asset. How can organizations discern trustworthy from untrustworthy partners, especially early on, so as to not waste time or resources on bad relationships? Academic/practical relevance: Like prior studies, we take the perspective that organizations rarely know whether a partner is trustworthy, but also that organizations often have some evidence of a partner's trustworthiness, even before interacting. We argue a qualitative study is needed to understand how people discern a partner's trustworthiness and the consequences of initial perceptions on the relationship trajectory. Methodology: We conduct an interview-based study of how people discern trustworthy partners in a setting where doing so is challenging: the education sector. Kindergarten-through-12th-grade schools must choose outside partners to rely on for resources or services the school cannot afford. Potential partners are numerous and of variable trustworthiness. Results: We find people use contextual factors as evidence of a potential partner's trustworthiness, such as the partner's institutional affiliations, physical proximity, and relationships with other schools. Sometimes the evidence indicates that a partner acts intrinsically trustworthily, regardless of these contextual factors. In other cases, the evidence indicates a partner acts contextually trustworthily, meaning partners follow through in some conditions but not others. Intrinsically trustworthy partners provide valuable but standardized resources or services. Contextually trustworthy partners provide the competitive advantage: customized resources that are not easily accessible by other schools. Managerial implications: People in organizations identify trustworthy partners via contextual factors, which helps them determine whether a partner acts trustworthily independent of context or conditional on context. The value of intrinsically trustworthy partners derives from their low risk and high quality, whereas the value of contextually trustworthy partners derives from their willingness to customize resources or services to some - but not all - organizations.
Read the full publication
A study of the lock-free tour problem and path-based reformulations
Year: 2020
Motivated by marathon course design, this article introduces a novel tour-finding problem, the Lock-Free Tour Problem (LFTP), which ensures that the resulting tour does not block access to certain critical vertices. The LFTP is formulated as a mixed-integer linear program. Structurally, the LFTP yields excessive subtour formation, causing the standard branch-and-cut approach to perform poorly, even with valid inequalities derived from locking properties of the LFTP. For this reason, we introduce path-based reformulations arising from a provably stronger disjunctive program, where disjunctions are obtained by fixing the visit orders in which must-visit edges are visited. In computational tests, the reformulations are shown to yield up to 100 times improvement in solution times. Additional tests demonstrate the value of reformulations for more general tour-finding problems with visit requirements and length restrictions. Finally, practical insights from the Bank of America Chicago Marathon are presented. Supplementary materials are available for this article. We refer the reader to the publisher’s online edition for additional experiments.
Read the full publication
Incorporating equity into the school bus scheduling problem
Year: 2019
We consider the school bus scheduling problem (SBSP) which simultaneously determines school bell times and route schedules. Often, the goal of the SBSP is to minimize the number of buses required by a school district. We extend a time-indexed integer programming model to incorporate additional considerations related to equity and efficiency. We seek to equitably reduce the disutilities associated with changing school start times via a minimax model, then propose a lexicographic minimax approach to improve minimax solutions. We apply our models to randomized instances based on a moderately-sized public school district to show the impact of incorporating equity.
Crowdsourcing Service-level Network Event Monitoring
Researcher(s): David R. Choffnes, Fabián E. Bustamante, Zihui Ge
Year: 2010
The user experience for networked applications is becoming a key benchmark for customers and network providers. Perceived user experience is largely determined by the frequency, duration and severity of network events that impact a service. While today’s networks implement sophisticated infrastructure that issues alarms for most failures, there remains a class of silent outages (e.g., caused by configuration errors) that are not detected. Further, existing alarms provide little information to help operators understand the impact of network events on services. Attempts to address this through infrastructure that monitors end-to-end performance for customers have been hampered by the cost of deployment and by the volume of data generated by these solutions. We present an alternative approach that pushes monitoring to applications on end systems and uses their collective view to detect network events and their impact on services - an approach we call Crowdsourcing Event Monitoring (CEM). This paper presents a general framework for CEM systems and demonstrates its effectiveness for a P2P application using a large dataset gathered from BitTorrent users and confirmed network events from two ISPs. We discuss how we designed and deployed a prototype CEM implementation as an extension to BitTorrent. This system performs online service-level network event detection through passive monitoring and correlation of performance in end-users’ applications.
Effectiveness of Innovative Speed Enforcement Techniques in Illinois
Researcher(s): Hani Mahmassani, Pei-Wei Lin
Year: 2009
Speed is a contributory factor in 29% of traffic-related fatal crashes occurring on Illinois highways. The Federal Highway Administration has identified Illinois as a Speed Focus State and has encouraged Illinois to develop a speed management program. Efforts have been made to address speed related fatalities and serious injuries on Illinois highways. In an era of a struggling economy, reducing resources, and competing needs, leaders within the public and private sector are turning to intelligence-driven assignments to produce the greatest gain with the least amount of investment. Once analyzed it is believed this information will provide the intelligence needs to develop more effective patrol strategies and procedures with the least amount of people.
This study attempts to identify the most effective speed enforcement patrol and saturation patrol procedures and methods, including effective enforcement duration and appropriate staffing level needs in order to more efficiently deploy valuable resources and maximize results. The results from this study will ultimately assist in the efforts to reduce speed-related traffic fatalities and serious injuries occurring on Illinois highways.
The analyses will results in a better understanding of the presence/absence and duration of speed enforcement on Illinois highways. Best practices for patrol and saturation patrol procedures will be provided from the research. In addition, there should be a better understanding of how the reduction of speed correlates to the reduction of severe crashes. The findings will allow IDOT and ISP to better allocate resources and ultimately reduce speed-related fatalities on Illinois highways.
Incorporating Reliability Performance Measures in Operations and Planning Modeling Tools
Researcher(s): Hani Mahmassani
Year: 2009
Dr. Mahmassani is one of three principal investigators on the team led by Delcan, Inc. to undertake this project. Northwestern’s role focuses on the theoretical and methodological underpinnings of integrated supply-demand models that incorporate reliability. The objectives are to advance the state of the art in planning and operations models to produce measures of reliability performance of proposed system changes, and determine how travel demand forecasting models can use reliability measures to produce more realistic estimates of travel patterns. Project L04 draws on the quantitative measures of reliability as well as the impacts of reliability on route choice, time-of-day choice, and mode choice substantiated in “Improving Our Understanding of How Highway Congestion and Pricing Affect Travel Demand, SHRP2 C04”.
This project is developing approaches and tools to incorporate reliability as an input as well a key output in traffic models used for both operations and planning applications. A unifying framework for reliability analysis is proposed, applicable in conjunction with any particle-based micro- or meso- simulation model that produces trajectories. Vehicle trajectories are introduced and discussed as a central building block in this framework. The methodology is demonstrated using a simulation-based DTA platform.
In addition, to capture travel time variability introduced by random events, a repeatable framework is developed for modeling and evaluating incidents and events. A key variability-inducing phenomenon is traffic flow breakdown, which is modeled as an inherently stochastic phenomenon with structural dependence on state variables of the system. Reliability-improving measures highlighted in the report include information supply and dynamic pricing, whose effectiveness increases considerably when applied in real-time on the basis of predicted conditions.
Finally, possible applications of travel time reliability in operations-oriented models are presented.
Incorporating Weather Impacts in Traffic Estimation and Prediction Systems
Researcher(s): Hani Mahmassani
Year: 2009
Dr. Mahmassani served as PI for this study conducted for FHWA under a subcontract to SAIC, Inc. The objectives of the project are to develop weather-sensitive traffic prediction and estimation models and incorporate them in existing traffic estimation and prediction systems. This includes enhancement of the capabilities in mesoscopic DTA tools to model traffic behavior under inclement weather, and capture user responses to inclement weather with and without the presence of advisory and control strategies.
As a result of this project, The DYNASMART TrEPS can now capture the effects of adverse weather on traffic patterns through both supply and demand side modifications to the model. New weather‐related features in DYNASMART include:
Weather Scenario Specification. DYNASMART allows users to specify various weather scenarios for the study network. It can be represented as either the network-wide weather condition or the link‐specific weather condition.
Weather Adjustment Factor. Users can define the effect of weather on supply‐side traffic parameters such as free flow speed and capacity based on three weather condition parameters: visibility (mile), rain precipitation intensity (inch/hr) and snow precipitation intensity (inch/hr) by means of Weather Adjustment Factors (WAF). DYNASMART applies user‐specified WAF to 18 supply‐side traffic properties for links within the impacted region to simulate traffic conditions under the weather condition. WAF can be obtained based on calibrated weather‐traffic flow relation.
Modeling Traffic Advisory and Control via Variable Message Signs (VMS) DYNASMART provides three weather‐related VMS operation functionalities : (1) Speed Reduction Warning – via a VMS warning sign indicating low visibility (e.g., fog) or slippery road (e.g. rain and snow), speed reduction behavior under adverse weather can be simulated; (2) Optional Detour – VMS suggests that travelers re-evaluate their current route based on the generalized cost that includes travel penalties of the added delays caused by adverse weather; and (3) Variable Speed Limit (VSL) – in DYNASMART, vehicle speed can be regulated through the speed limits posted on VMS in correspondence with prevailing weather conditions.
iTRAC: Intelligent Compression of Traffic Video
Researcher(s): Sotirios A. Tsaftaris, Aggelos K. Katsaggelos
Year: 2009
Non-intrusive video imaging sensors are commonly used in traffic monitoring and surveillance. For some applications it is necessary to transmit the video data over communication links. However, due to increased requirements of bitrate this means either expensive wired communications links are used or the video data are heavily compressed to not exceed the allowed communications bandwidth. Current video imaging solutions utilize old video compression standards and require dedicated wired communication lines. Recently H.264 (the newest video compression standard) has been proposed to be used in transportation applications. However, most video compression algorithms are not optimized for traffic video data and do not take into account the possible data analysis that will follow (either in real time at the control center or offline). As a result of compression, the visual quality of the data may be low, but more importantly, as out research efforts in vehicle tracking have shown, the tracking accuracy and efficiency is severely affected. We propose to develop a set of algorithms (implemented in the form of a software module) that will operate with the constraints of the H.264 video compression standard. It will aim to improve the performance of traffic tracking applications while using the same transmission bandwidth or equivalently maintaining the same level of performance while reducing the bandwidth usage. The output of our project will be a software module that will be integrated into the logic of hardware video compression encoders.
Video Traffic Analysis for Abnormal Event Detection
Researcher(s): A.K. Katsaggelos, S. Tsaftaris, Y. Wu
Year: 2008
We have developed statistical approaches for the detection of abnormal video events for surveillance applications. We proposed to extend such approached and apply them towards the classification of vehicle trajectories in roadway video data for analysis and mitigation of traffic congestion. With the proposed approach, traffic information will first be analyzed off-line in an automated fashion. We will examine both the behavior of each vehicle independently but also its interaction with other vehicles. The effect of abnormal events onto incoming traffic will be a central objective of this investigation. Our goal is to provide the foundations of a system that will allow the off-line analysis of video data. The results of the off-line analysis could be utilized in two major ways: (i) by transportation officials to consider revising transportation rules and regulations and (ii) in developing on-line technologies for tracking to most disruptive abnormal events and minimizing their effect in creating congestion, via, for example, deployment of emergency vehicles, timely response of transportation agencies, and roadside information display systems.
Modeling Human Behavior and Intelligent Agent-Based Traffic Flow Simulation
Researcher(s): Hani Mahmassani
Year: 2007
This three year study, funded by the National Science Foundation, put forward a comprehensive, multidisciplinary research approach to characterize and model human cognitive driving behavior and subsequent response in traffic flow systems. Specifically, the dynamics of driver behavior, taken at the individual level and as part of a group, evolving over time and space will be systematically studies as a complex system. By developing behavior-based models of human decision-making in traffic situations and integrating the behavior models in computer simulation systems, the study addresses fundamental questions in traffic science and promises to improve prevailing understanding of traffic flow phenomena as well as the fidelity and reliability of the current state of the art of traffic flow simulation. Of particular interest in this study is driving behavior under extreme conditions, including inclement weather, natural and man-made disasters.