Hani Mahmassani | Northwestern University (original) (raw)
Papers by Hani Mahmassani
Transportation Research Board 95th Annual Meeting, 2016
Using data from online location-based social networks, the aim of this paper is to explore the sp... more Using data from online location-based social networks, the aim of this paper is to explore the spatiality of destinations in the context of social networks, and the influence of one’s social network on travelers’ destination choices through check-in behavior. Analysis results show that social relationships play a role in travelers’ destination choices, and that distance plays a strong role in social networks and in location choice. By comparing checking-in behavior of travelers in two social networks identified in two metropolitan areas (Chicago and New York City), and examining interactions in the largest communities in each social network, results show that the denser a social network is, the greater the likelihood that travelers are influenced by their friends in their choice of destination. However, travelers’ own experience appears to exert greater influence on their decision making than friendship.
In applying travel choice models to predict demand in practice, it is generally assumed that the ... more In applying travel choice models to predict demand in practice, it is generally assumed that the choice set is known and static, that travelers are aware of the attributes of the available alternatives, and that the individual choices satisfy the needs/preferences of the decision-maker viewed independently (of others). This simplification of the underlying behavioral processes becomes less acceptable for many applications, especially when modeling choices with potentially large choice sets, attributes that are perceived differently by each user and choices where decision-makers interact heavily with each other. Such compromises have been adopted mainly because of a lack of data and also because of computational and theoretical convenience in transportation and econometric models. New data sources and modern survey technologies have potential to overcome the lack of detail in data, as well as deliver high levels of spatial and temporal specificity and accuracy. However, they also give rise to new challenges that must be overcome in order to make them usable for real world applications. This paper discusses the opportunities as well as some of the challenges of incorporating social media data in choice models, illustrated by analyzing the data from a popular on-line location-based social network called Brightkite (1). The data set contains dynamic user check-in information with time stamp and coordinates as well as an undirected social network of its users. The premise of this paper is that social networks influence people’s resource allocation decisions in planning and executing activities, and that the manifestation of such decisions in their travel patterns can be observed in the data set at hand. Location-based social networking data provide an important new dimension in understanding travel choice behavior, by providing high levels of location and time accuracy over longer time frames in conjunction with explicit friendship network information. Such data allow for studying location choice dynamics and social networking aspects explicitly. However, such data can also suffer from well-known biases, due to the sporadic nature of check-ins (which leads to non-random censoring), inferring the choice set, noisy data and self-selection. Furthermore, with the rapid diffusion of information and frequent social communication taking place through various channels nowadays, not all travelers are aware of the same information, and they likely perceive information differently depending on the reliability, latency and affinity of the source. Travelers learn about changes through various information sources, including social communication with others. For example, the formation of destination choice sets will be influenced not only by external factors but also personal perceptions. In these cases, consideration of the opinion formation process about a new destination and the role of information diffusion through social communication become important elements of the dynamics of the choice set adoption and formation process. The interpersonal mechanisms behind opinion formation, such as word-of-mouth (2) , mass-media (3) , direct experience (4) and belief learning (5), may correlate with information exchange. Thus, exploring the choice set generation process also requires investigating the dynamics of social networks and repeated behavior. One would expect that people exhibit strong periodic behavior in traveling between their homes and workplaces, and also in visiting leisure locations. Moreover, daily travel behavior patterns, especially non-frequent movements such as long distance trips, may be shaped by social networks, that influence the probability positively to visit locations close to our friends, or locations that were visited by our social network before us. 0 0 1 645 3677 Northwestern University 30 8 4314 14.0 Normal 0 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; line-height:106%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri; mso-fareast-language:ZH-CN;} From a preliminary analysis of the Brightkite data, it can be found that friendship status influenced people’s destination choices, especially in a denser social network. Travelers are more likely to visit places that their friends have previously visited, all else being equal. However, the influence of one’s experience on the likelihood of returning to previously visited locations is even stronger. This indicates that the opinion of others matters, but not as much as one’s own experience. KEY WORDS: Social network, Check-ins, Choice set generation, Travel behavior dynamics REFERENCES 1. http://en.wikipedia.org/wiki/Brightkite . 2. Gladwell, M. The Tipping Point: How…
Transportation Research Board 94th Annual Meeting, 2015
Travel behaviour and society, Apr 1, 2020
Transportation Research Record, Oct 26, 2018
Travel behaviour and society, Oct 1, 2018
Transportation Research Part C-emerging Technologies, Mar 1, 2020
Transportation Research Board 98th Annual MeetingTransportation Research Board, 2019
Transportation Research Board 96th Annual MeetingTransportation Research Board, 2017
Transportation Research Board 95th Annual MeetingTransportation Research Board, 2016
Carpooling and other forms of ride sharing are of growing significance in urban travel as environ... more Carpooling and other forms of ride sharing are of growing significance in urban travel as environment- friendly and sustainable travel alternatives. With the widespread use of location sensing technology, spatial data is easily accessible and widely available. Massive volumes of spatial datasets provide opportunities to work on spatial data mining. Clustering vehicle trajectories enables identification of potential carpool routes. The objective of this paper is to detect common sub-paths in a road network through the analysis of trajectory data and to propose potential routes for carpool commuters. The clustering algorithm was applied to a large set of meso-level simulated trajectory data in the Chicago area. The identified sub-path clusters provide a basis for detecting likely carpool routes. The carpool participation sensitivity analysis and the simulation test for departure and arrival guaranteed routes demonstrated that carpool programs could contribute to congestion relief and travel time reduction.
Transportation Research Board 95th Annual MeetingTransportation Research Board, 2016
In order to evaluate the effectiveness of traffic management studies, it is desirable to identify... more In order to evaluate the effectiveness of traffic management studies, it is desirable to identify scenarios corresponding to different factors that affect the system’s operational performance. Cluster analysis applied to historical data is an approach that has gained popularity in practice to identify scenarios for such applications. In this paper, a weighted clustering method is proposed, in which each data point is a temporally matched, and the various variables or indices of interest are combined in a weighted measure that serves as a basis for the clustering process. In this study, the variables considered are historical values of the traffic flow rate, and various weather condition indicators. A detailed procedure for clustering this weighted dataset is developed based on the K-means clustering algorithm and applied to a subnetwork in Chicago. To evaluate the clustering results, system performance consistency checking is conducted. By selecting a good matching scenario with current situation from corresponding clusters, the traffic patterns are estimated and the effect of estimated traffic patterns are analyzed using an on-line traffic estimation and prediction system (TrEPS). The results indicate that the weighted clustering process can effectively cluster the traffic demand under different weather conditions, resulting in improved estimation and prediction results for traffic management applications.
Transportation Research Record, 2015
Transportation Research Part E-logistics and Transportation Review, May 1, 2020
IEEE Transactions on Intelligent Transportation Systems, Aug 1, 2018
Transportation Research Part C-emerging Technologies, Dec 1, 2017
Transportation Research Board 97th Annual MeetingTransportation Research Board, 2018
Transportation Research Board 97th Annual MeetingTransportation Research Board, 2018
Transportation Research Board 96th Annual MeetingTransportation Research Board, 2017
Transportation Research Board 95th Annual Meeting, 2016
Using data from online location-based social networks, the aim of this paper is to explore the sp... more Using data from online location-based social networks, the aim of this paper is to explore the spatiality of destinations in the context of social networks, and the influence of one’s social network on travelers’ destination choices through check-in behavior. Analysis results show that social relationships play a role in travelers’ destination choices, and that distance plays a strong role in social networks and in location choice. By comparing checking-in behavior of travelers in two social networks identified in two metropolitan areas (Chicago and New York City), and examining interactions in the largest communities in each social network, results show that the denser a social network is, the greater the likelihood that travelers are influenced by their friends in their choice of destination. However, travelers’ own experience appears to exert greater influence on their decision making than friendship.
In applying travel choice models to predict demand in practice, it is generally assumed that the ... more In applying travel choice models to predict demand in practice, it is generally assumed that the choice set is known and static, that travelers are aware of the attributes of the available alternatives, and that the individual choices satisfy the needs/preferences of the decision-maker viewed independently (of others). This simplification of the underlying behavioral processes becomes less acceptable for many applications, especially when modeling choices with potentially large choice sets, attributes that are perceived differently by each user and choices where decision-makers interact heavily with each other. Such compromises have been adopted mainly because of a lack of data and also because of computational and theoretical convenience in transportation and econometric models. New data sources and modern survey technologies have potential to overcome the lack of detail in data, as well as deliver high levels of spatial and temporal specificity and accuracy. However, they also give rise to new challenges that must be overcome in order to make them usable for real world applications. This paper discusses the opportunities as well as some of the challenges of incorporating social media data in choice models, illustrated by analyzing the data from a popular on-line location-based social network called Brightkite (1). The data set contains dynamic user check-in information with time stamp and coordinates as well as an undirected social network of its users. The premise of this paper is that social networks influence people’s resource allocation decisions in planning and executing activities, and that the manifestation of such decisions in their travel patterns can be observed in the data set at hand. Location-based social networking data provide an important new dimension in understanding travel choice behavior, by providing high levels of location and time accuracy over longer time frames in conjunction with explicit friendship network information. Such data allow for studying location choice dynamics and social networking aspects explicitly. However, such data can also suffer from well-known biases, due to the sporadic nature of check-ins (which leads to non-random censoring), inferring the choice set, noisy data and self-selection. Furthermore, with the rapid diffusion of information and frequent social communication taking place through various channels nowadays, not all travelers are aware of the same information, and they likely perceive information differently depending on the reliability, latency and affinity of the source. Travelers learn about changes through various information sources, including social communication with others. For example, the formation of destination choice sets will be influenced not only by external factors but also personal perceptions. In these cases, consideration of the opinion formation process about a new destination and the role of information diffusion through social communication become important elements of the dynamics of the choice set adoption and formation process. The interpersonal mechanisms behind opinion formation, such as word-of-mouth (2) , mass-media (3) , direct experience (4) and belief learning (5), may correlate with information exchange. Thus, exploring the choice set generation process also requires investigating the dynamics of social networks and repeated behavior. One would expect that people exhibit strong periodic behavior in traveling between their homes and workplaces, and also in visiting leisure locations. Moreover, daily travel behavior patterns, especially non-frequent movements such as long distance trips, may be shaped by social networks, that influence the probability positively to visit locations close to our friends, or locations that were visited by our social network before us. 0 0 1 645 3677 Northwestern University 30 8 4314 14.0 Normal 0 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; line-height:106%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri; mso-fareast-language:ZH-CN;} From a preliminary analysis of the Brightkite data, it can be found that friendship status influenced people’s destination choices, especially in a denser social network. Travelers are more likely to visit places that their friends have previously visited, all else being equal. However, the influence of one’s experience on the likelihood of returning to previously visited locations is even stronger. This indicates that the opinion of others matters, but not as much as one’s own experience. KEY WORDS: Social network, Check-ins, Choice set generation, Travel behavior dynamics REFERENCES 1. http://en.wikipedia.org/wiki/Brightkite . 2. Gladwell, M. The Tipping Point: How…
Transportation Research Board 94th Annual Meeting, 2015
Travel behaviour and society, Apr 1, 2020
Transportation Research Record, Oct 26, 2018
Travel behaviour and society, Oct 1, 2018
Transportation Research Part C-emerging Technologies, Mar 1, 2020
Transportation Research Board 98th Annual MeetingTransportation Research Board, 2019
Transportation Research Board 96th Annual MeetingTransportation Research Board, 2017
Transportation Research Board 95th Annual MeetingTransportation Research Board, 2016
Carpooling and other forms of ride sharing are of growing significance in urban travel as environ... more Carpooling and other forms of ride sharing are of growing significance in urban travel as environment- friendly and sustainable travel alternatives. With the widespread use of location sensing technology, spatial data is easily accessible and widely available. Massive volumes of spatial datasets provide opportunities to work on spatial data mining. Clustering vehicle trajectories enables identification of potential carpool routes. The objective of this paper is to detect common sub-paths in a road network through the analysis of trajectory data and to propose potential routes for carpool commuters. The clustering algorithm was applied to a large set of meso-level simulated trajectory data in the Chicago area. The identified sub-path clusters provide a basis for detecting likely carpool routes. The carpool participation sensitivity analysis and the simulation test for departure and arrival guaranteed routes demonstrated that carpool programs could contribute to congestion relief and travel time reduction.
Transportation Research Board 95th Annual MeetingTransportation Research Board, 2016
In order to evaluate the effectiveness of traffic management studies, it is desirable to identify... more In order to evaluate the effectiveness of traffic management studies, it is desirable to identify scenarios corresponding to different factors that affect the system’s operational performance. Cluster analysis applied to historical data is an approach that has gained popularity in practice to identify scenarios for such applications. In this paper, a weighted clustering method is proposed, in which each data point is a temporally matched, and the various variables or indices of interest are combined in a weighted measure that serves as a basis for the clustering process. In this study, the variables considered are historical values of the traffic flow rate, and various weather condition indicators. A detailed procedure for clustering this weighted dataset is developed based on the K-means clustering algorithm and applied to a subnetwork in Chicago. To evaluate the clustering results, system performance consistency checking is conducted. By selecting a good matching scenario with current situation from corresponding clusters, the traffic patterns are estimated and the effect of estimated traffic patterns are analyzed using an on-line traffic estimation and prediction system (TrEPS). The results indicate that the weighted clustering process can effectively cluster the traffic demand under different weather conditions, resulting in improved estimation and prediction results for traffic management applications.
Transportation Research Record, 2015
Transportation Research Part E-logistics and Transportation Review, May 1, 2020
IEEE Transactions on Intelligent Transportation Systems, Aug 1, 2018
Transportation Research Part C-emerging Technologies, Dec 1, 2017
Transportation Research Board 97th Annual MeetingTransportation Research Board, 2018
Transportation Research Board 97th Annual MeetingTransportation Research Board, 2018
Transportation Research Board 96th Annual MeetingTransportation Research Board, 2017
This paper introduces an integrated mode choice–multimodal transit assignment model and solution ... more This paper introduces an integrated mode choice–multimodal transit assignment model and solution procedure intended for large-scale urban applications. The cross-nested logit mode choice model assigns travelers to car, transit, or park-and-ride. The dynamic multimodal transit assignment–simulation model determines minimum hyperpaths and assigns and simulates transit and park-and-ride travelers iteratively until the network approaches a state of equilibrium. After a given number of iterations, the updated transit network travel times are fed into the mode choice model and the model reassigns travelers to transit, car, or park-and-ride. The outer feedback loop between the mode choice model and the transit assignment model continues until the mode probabilities for each traveler do not change between iterations. A unique contribution of the method presented in this paper is that it reaches mode choice convergence with the use of disaggregate agents (travelers) instead of aggregate modal flows at the origin–destination level. The integrated model is successfully implemented on the Chicago Transit Agency's bus and train network in Illinois. Different procedures for reaching convergence are tested; the results suggest that a gap-based formulation is more efficient than the method of successive averages.
Abstract This paper presents a gap-based solution method for the time-dependent transit assignmen... more Abstract This paper presents a gap-based solution method for the time-dependent transit assignment problem with vehicle capacity constraints. A two-level, simulation-based methodology is proposed, which finds the least cost hyperpaths at the upper level and performs the assignment of transit travelers on the hyperpaths at the lower level. The detailed simulation of travelers and vehicles at the lower level allows modelers to capture transit network complexities such as transfers/missed connections, receiving a seat/standing and boarding/being rejected to board. This ‘hard’ implementation of vehicle capacity constraints at the lower level is aggregated into ‘soft constraints’ at the upper level for the least cost hyperpath calculation. Using a gap-based assignment procedure, user equilibrium is reached on large-scale networks in a computationally efficient manner. The algorithm is tested on the large-scale Chicago Transit Authority network. The gap-based approach outperforms the commonly used method of successive averages approach in terms of rate of convergence and quality of results. Furthermore, sensitivity analyses with respect to network parameters illustrate the robustness of the proposed two-level solution procedure.
The rapid onset of the COVID-19 pandemic in March 2020 marked a challenging time for the country ... more The rapid onset of the COVID-19 pandemic in March 2020 marked a challenging time for the country and the U.S. freight industry. Manufacturing slowed, consumer purchasing patterns changed, and for many, shopping moved online. The freight industry suffered a sharp decline in shipments, followed by a surprisingly quick rise. The movement of goods by freight rail had to quickly adapt to meet dynamically changing demand and volatile supply patterns. Despite this disruption, freight rail showed a great deal of resilience and reliability. This report addresses how the rail industry met the challenge of this whiplash in demand, explores impediments to performance during this period and looks beyond the crisis towards the future for the rail sector. The assessment outlined in this report was completed by researchers at Northwestern University’s Transportation Center (NUTC). The results show how the U.S. freight rail industry was an essential component of pandemic resilience, demonstrating a high level of adaptability to meet consumer and business demands.
Supported by a grant from the Association of Equipment Manufacturers (AEM), the Transportation Ce... more Supported by a grant from the Association of Equipment Manufacturers (AEM), the Transportation Center undertook an exploration of the factors, needs, and opportunities facing U.S. transportation infrastructure in the next 35 years. The objective of the study was not to forecast the future, but to frame the possibilities and thus to inform the public and policy makers about future needs for transportation infrastructure. The outcome of this study is Mobility 2050: A Vision for Transportation Infrastructure.