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This study builds theoretical explanations and empirical examinations of transit access and urban form. It explains how transit access to jobs drops with network distance from the Central Business District (CBD), ceteris paribus, and introduces the urban space-time structure. This is empirically examined in the 45 most populated American cities. The analysis finds, regardless of the city, transit access declines as one moves out from the center, and in most cities, transit access decays from a surfeit of employment to relative scarcity. In this transition, the transit network acts as a “catalyst” to induce access to CBD employments centralization. The analysis also declares that urban structure defined by transit access is a fluid concept. This changes the traditional urban structure definition as it can make a CBD centric city dispersed and contrariwise. The strength of inverse relationship between the network distance from the center and transit access is a function of the travel-time threshold and follows the law of diminishing returns. The vertex point of the function indicates the absolute maximum CBD centricity. The urban structure, indeed, shifts from CBD decentralized to CBD centralized and begins shifting to CBD decentralized by an increase in the transit travel-time threshold. This is the product of mobility and place, and argues that the transport network has grown by a policy that permits CBD concentration, at least in the short run. While it is not clear, long-term considerations of equity may modify this growth to one of concentrated CBD decentralization. The concentration is typical of the take-off stage of the transport network, and that equalization takes place as the network matures.
Alireza Ermagun. Transit access and urban space-time structure of American cities. Journal of Transport Geography 2021, 93, 103066 .
AMA StyleAlireza Ermagun. Transit access and urban space-time structure of American cities. Journal of Transport Geography. 2021; 93 ():103066.
Chicago/Turabian StyleAlireza Ermagun. 2021. "Transit access and urban space-time structure of American cities." Journal of Transport Geography 93, no. : 103066.
This article seeks to understand the potential response of drivers when they encounter compromised Dynamic Message Signs (DMS). The findings are built on the self-reported response of 4,706 participants in a Stated Preference (SP) survey conducted between November 2018 and December 2018 in the United States. The findings show the response of drivers to the “Downtown Under Terrorist Attack” message falls into route divergence, speed change, and distraction, and the likelihood of route divergence, distraction, and a slowdown is significantly more than either stopping or speeding up. The possibility of a response, however, varies depending on socioeconomic characteristics, attitudinal characteristics, and driving behavior. It is highlighted (1) female and young drivers are more probable to detour, to change speed, or to be distracted, (2) drivers who are familiar with DMS, encounter it frequently, or pay attention to its content, have a higher chance of detouring or changing speed, while they are less likely to be distracted, and (3) technology-friendly drivers are likely to detour or slow down. From the distraction model, it is further inferred that drivers are distracted cognitively, visually, and manually. The findings have implications for researchers and federal, state, and local agencies who are aware of the consequences of cybersecurity threats for the operation and profitability of the transport network. They, for example, assist transport planners in prioritizing equipment security efforts and resource allocation to the areas of greatest risk, and help to prepare contingency plans based on drivers’ behavioral response.
Alireza Ermagun; Kaveh Bakhsh Kelarestaghi; Kevin Heaslip. Drivers’ self-reported responses to a potentially realistic fabricated road sign message. Transportation Research Part F: Traffic Psychology and Behaviour 2021, 78, 103 -118.
AMA StyleAlireza Ermagun, Kaveh Bakhsh Kelarestaghi, Kevin Heaslip. Drivers’ self-reported responses to a potentially realistic fabricated road sign message. Transportation Research Part F: Traffic Psychology and Behaviour. 2021; 78 ():103-118.
Chicago/Turabian StyleAlireza Ermagun; Kaveh Bakhsh Kelarestaghi; Kevin Heaslip. 2021. "Drivers’ self-reported responses to a potentially realistic fabricated road sign message." Transportation Research Part F: Traffic Psychology and Behaviour 78, no. : 103-118.
In a progressively interconnected world, the loss of system resilience has consequences for human health, the economy, and the environment. Research has exploited the science of networks to explain the resilience of complex systems against random attacks, malicious attacks, and the localized attacks induced by natural disasters or mass attacks. Little is known about the elucidation of system recovery by the network topology. This study adds to the knowledge of network resilience by examining the nexus of recoverability and network topology. We establish a new paradigm for identifying the recovery behavior of networks and introduce the recoverability measure. Results indicate that the recovery response behavior and the recoverability measure are the function of both size and topology of networks. In small sized networks, the return to recovery exhibits homogeneous recovery behavior over topology, while the return shape is dispersed with an increase in the size of network. A network becomes more recoverable as connectivity measures of the network increase, and less recoverable as accessibility measures of network increase. Overall, the results not only offer guidance on designing recoverable networks, but also depict the recovery nature of networks deliberately following a disruption. Our recovery behavior and recoverability measure has been tested on 16 distinct network topologies. The relevant recovery behavior can be generalized based on our definition for any network topology recovering deliberately.
Alireza Ermagun; Nazanin Tajik. Recovery patterns and physics of the network. PLOS ONE 2021, 16, e0245396 .
AMA StyleAlireza Ermagun, Nazanin Tajik. Recovery patterns and physics of the network. PLOS ONE. 2021; 16 (1):e0245396.
Chicago/Turabian StyleAlireza Ermagun; Nazanin Tajik. 2021. "Recovery patterns and physics of the network." PLOS ONE 16, no. 1: e0245396.
Crowd-shipping is an innovative delivery model using digital platforms to match the demand for shipments with supply using excess transport capacity and drivers from the crowd. This sharing economy delivery concept has attracted growing attention to address the pressing challenges of urban goods deliveries. Little is known about the actual performance of crowd-shipping platforms due to limited data-availability and operational transparency. A particular challenge is that part of the delivery outcome is determined in the platform's digital space related to bidding and matching of supply and demand, followed by a real-world delivery operation, typically carried out by non-expert couriers. This paper provides the first comprehensive analysis of the entire crowd-shipping process from the bidding stage, through shipment acceptance, pickup, and final delivery. Using parametric hazard modeling applied to a unique U.S. national database of 16,850 crowd-shipping delivery instances, we examine which factors play a role in each phase of the delivery process. The findings illustrate that shipping requests and packages, built environment, and socioeconomic characteristics have a variable impact on each delivery stage. In particular, posting in the morning or evening hours and for business-to-consumer shipments significantly accelerates the digital phase, but has no effects on the final delivery phase. Moreover, the results reveal that performance loss occurs non-uniformly in the platform process, with a more significant loss in delivery rates related to the digital posting and bidding. A more substantial loss of delivery speed performance occurs in converting from digital to real delivery in negotiating the pickup arrangement. Crowd-shipping companies will benefit from the research to improve the management of their peer-to-peer-based mechanism.
Alireza Ermagun; Amanda Stathopoulos. Crowd-shipping delivery performance from bidding to delivering. Research in Transportation Business & Management 2020, 100614 .
AMA StyleAlireza Ermagun, Amanda Stathopoulos. Crowd-shipping delivery performance from bidding to delivering. Research in Transportation Business & Management. 2020; ():100614.
Chicago/Turabian StyleAlireza Ermagun; Amanda Stathopoulos. 2020. "Crowd-shipping delivery performance from bidding to delivering." Research in Transportation Business & Management , no. : 100614.
This study proposes a two-step pattern detection methodology for dynamic bike share station traffic prediction using historic traffic and spatiotemporal characteristics. The model is developed on the 15-minute aggregated Washington, D.C. Capital Bikeshare data to predict bike share station traffic for both short- and long-term horizons ranging from 15 min to 4 h. The results show the prediction accuracy equals 100% for 15-minute, 1-hour, and 2-hour horizons and slightly more than 95% for 3-hour and 4-hour horizons at the system level. Not surprisingly, the prediction accuracy drops at the station level. For 15-minute and 1-hour horizons, the prediction accuracy equals 77% and 82%, and it ranges from 24% to 31% for 2-hour, 3-hour, and 4-hour horizons. The results also show that temporal characteristics contribute more than spatial characteristics in the short-time horizons, but the contribution is flipped for long-time horizons. The proposed models have the capacity to estimate bike share traffic for both short- and long-time horizons in less than 20 s of runtime, which illustrates the practicality of the models in dynamic bike sharing traffic prediction, and the potential of the proposed model to be updated in real-time and incorporate the most recent observations into predictions.
Soheil Sohrabi; Alireza Ermagun. Dynamic bike sharing traffic prediction using spatiotemporal pattern detection. Transportation Research Part D: Transport and Environment 2020, 90, 102647 .
AMA StyleSoheil Sohrabi, Alireza Ermagun. Dynamic bike sharing traffic prediction using spatiotemporal pattern detection. Transportation Research Part D: Transport and Environment. 2020; 90 ():102647.
Chicago/Turabian StyleSoheil Sohrabi; Alireza Ermagun. 2020. "Dynamic bike sharing traffic prediction using spatiotemporal pattern detection." Transportation Research Part D: Transport and Environment 90, no. : 102647.
This article studies the equity of transit accessibility in the City of Chicago. We measure the accessibility of different cohorts including minority and low-income populations, the elderly, people with disabilities, those with lower education levels, and households without a car to six different destinations by public transit. The destinations are jobs, parks, groceries, hospitals, schools, and libraries. We show that there are clear inequalities across cohorts in the distribution of benefits that the transit system provides as measured by the number of reachable valued destinations. The results indicate that areas of low accessibility have a higher percentage of African-Americans, Hispanics, Asians, low-income workers, low-educated citizens, and the elderly. The most affected cohort are low-income workers, for whom access to jobs, parks, groceries, hospitals, and libraries decline as their number grows. The findings also highlight that inequities are most severe, in order, to jobs, hospitals, and grocery stores when examining the different cohorts. While transit agencies must deploy service with the existing demand in mind, the observed inequities behoove decision makers to make accessibility and equity considerations explicit in transit service decisions.
Alireza Ermagun; Nebiyou Tilahun. Equity of transit accessibility across Chicago. Transportation Research Part D: Transport and Environment 2020, 86, 102461 .
AMA StyleAlireza Ermagun, Nebiyou Tilahun. Equity of transit accessibility across Chicago. Transportation Research Part D: Transport and Environment. 2020; 86 ():102461.
Chicago/Turabian StyleAlireza Ermagun; Nebiyou Tilahun. 2020. "Equity of transit accessibility across Chicago." Transportation Research Part D: Transport and Environment 86, no. : 102461.
This study sheds light on the travel behavior of drivers when they encounter fabricated messages in work zones. Using the response of 4302 participants to a stated preference survey, we develop a multivariate ordered response model and a structural equation model to study speed change and distraction response behavior. The results of our models for fabricated announcements signify that drivers normally follow the announcement and are affected likewise. The selected socioeconomic and attitudinal variables are shown to have mixed impacts in our speed and distraction models. Some variables are statistically significant for each model, while other variables are only statistically significant for one of the models. For instance, drivers that have seen a fabricated announcement before are less likely to speed up when encountering the message, while drivers who rely on technology for their daily travels are more likely to be distracted. Higher income is shown in our models to signify undesirable behaviors: speeding up and being distracted. Contrastingly, female drivers are less likely to do nothing or be distracted by the announcement. The findings, taken together, have implications for researchers and practitioners. First, they illustrate how cyberattacks can destabilize traffic in work zone and put the life of work zone crew members in jeopardy. Second, they explain the degree of compliance with compromised dynamic message signs.
Alireza Ermagun; Kaveh Bakhsh Kelarestaghi; Megan Finney; Kevin Heaslip. “Speed Up to Hit the Worker”: Impact of hacked road signs on work zone safety. International Journal of Transportation Science and Technology 2020, 10, 49 -59.
AMA StyleAlireza Ermagun, Kaveh Bakhsh Kelarestaghi, Megan Finney, Kevin Heaslip. “Speed Up to Hit the Worker”: Impact of hacked road signs on work zone safety. International Journal of Transportation Science and Technology. 2020; 10 (1):49-59.
Chicago/Turabian StyleAlireza Ermagun; Kaveh Bakhsh Kelarestaghi; Megan Finney; Kevin Heaslip. 2020. "“Speed Up to Hit the Worker”: Impact of hacked road signs on work zone safety." International Journal of Transportation Science and Technology 10, no. 1: 49-59.
This paper empirically studies the matching and delivery process in a major crowd-sourced delivery platform. The aim is to develop models to understand and predict crowd-shipping delivery performance and using the findings to design incentives to improve user experiences as well as system performance. We apply the random forest machine learning algorithm to predict the shipment status of 14,858 crowd-shipping requests recorded between January 2015 and December 2016 throughout the U.S. The models are used to predict three phases of the crowd-shipping performance, namely bidding, acceptance, and delivery, using shipping request, built-environment, and socioeconomic features as explanatory variables. The results demonstrate that the context of the shipment provides strong predictive performance even when shipping request and package information is unknown. Calculating the sensitivity of bid probability, we show that offering a higher reward and posting a shipping request in the morning has the largest effect on the probability to secure a bid. We also find that larger shipments, out-of-state destinations, and peer-to-peer shipments lead to higher sensitivity, likely reflecting the higher perceived risks of such transactions. In practice, the models presented in this study show promise in their ability to effectively predict shipment status in real time. We illustrate a valuable application of the sensitivity analysis derived from the random forest models to develop customer-tailored crowd-shipping smartphone applications. Based on the data mined from past deliveries, customers are given empirically based delivery forecasts for their specific package request and can modify delivery requests to increase their odds of delivery. We find that pricing is the variable with the highest potential to increase delivery probability followed by the timing of the request.
Alireza Ermagun; Aymeric Punel; Amanda Stathopoulos. Shipment status prediction in online crowd-sourced shipping platforms. Sustainable Cities and Society 2019, 53, 101950 .
AMA StyleAlireza Ermagun, Aymeric Punel, Amanda Stathopoulos. Shipment status prediction in online crowd-sourced shipping platforms. Sustainable Cities and Society. 2019; 53 ():101950.
Chicago/Turabian StyleAlireza Ermagun; Aymeric Punel; Amanda Stathopoulos. 2019. "Shipment status prediction in online crowd-sourced shipping platforms." Sustainable Cities and Society 53, no. : 101950.
Crowd logistics is a novel shipping concept where delivery operations are carried out by using existing resources, namely vehicle capacity and drivers from the crowd, thereby offering potential for economic, social, and environmental benefits. Despite the promise of this new logistics model, little is known about its actual functioning, performance, and impact. This paper presents a pioneering study of the performance of a real crowd-shipping system in the U.S. using empirical data from 2 years of operations. We contribute to the literature by: (1) defining performance metrics and developing models that account for the specificity of crowd-shipping systems by distinguishing the essential stages from bidding to acceptance and delivery of shipments, (2) identifying the significant covariates, including shipment features, built environment, and socio-demographic factors giving rise to different delivery performance outcomes, and (3) deriving sensitivity analysis to study the performance and implications of crowd-shipping in urban and suburban areas. The analysis is formalized as two-level nested logit models with nests representing bidding and delivery outcomes. The results show that not only does the delivery outcome performance vary significantly between urban and suburban areas, but the explanatory factors also vary significantly for the two contexts. Additionally, several factors have ambiguous impacts depending on the stage. Larger shipment size (versus strict deadlines) leads to increasing (decreasing) the likelihood of bids being placed, while having the opposite effect when it comes to the delivery phase. The findings highlight the need for developing different strategies to foster and improve the performance of this novel system depending on both the urban–suburban shipping context and the stage of delivery.
Alireza Ermagun; Ali Shamshiripour; Amanda Stathopoulos. Performance analysis of crowd-shipping in urban and suburban areas. Transportation 2019, 47, 1955 -1985.
AMA StyleAlireza Ermagun, Ali Shamshiripour, Amanda Stathopoulos. Performance analysis of crowd-shipping in urban and suburban areas. Transportation. 2019; 47 (4):1955-1985.
Chicago/Turabian StyleAlireza Ermagun; Ali Shamshiripour; Amanda Stathopoulos. 2019. "Performance analysis of crowd-shipping in urban and suburban areas." Transportation 47, no. 4: 1955-1985.
This study explores the interdependence between passenger travel experience and service quality in the airline industry for ten geographical regions across the globe. We extract 40,510 passenger reviews and rating information from Skytrax dataset between October 2011 and January 2018. To understand whether and to what extent passenger travel experience varies across geographical regions and their flight classes, we test sentiment score analysis of the reviews and path analysis methods. The results support the hypothesis that the geographical regions shaped by the country of residence of passengers impact travel experience, perception, and evaluation of airline services. North American passengers complain more about their national airline, while East and Southeast Asian passengers are more satisfied with Asian airlines. North American passengers care essentially about the money they pay for their flight and they pay less attention to in-flight services. East Asian passengers care more about in-flight services. Across all geographical regions, seat comfort is the most important factor to evaluate the value for money of the flight. Cabin staff service, however, is the main feature to rate overall flight experience. The results also corroborate that the expectation of passengers is different between the first or business classes and the economy class. Passengers in first or business class are more concerned about seat comfort, food and beverages, and in-flight entertainment. Passengers in economy class are more concerned about the value for money.
Aymeric Punel; Lama Al Hajj Hassan; Alireza Ermagun. Variations in airline passenger expectation of service quality across the globe. Tourism Management 2019, 75, 491 -508.
AMA StyleAymeric Punel, Lama Al Hajj Hassan, Alireza Ermagun. Variations in airline passenger expectation of service quality across the globe. Tourism Management. 2019; 75 ():491-508.
Chicago/Turabian StyleAymeric Punel; Lama Al Hajj Hassan; Alireza Ermagun. 2019. "Variations in airline passenger expectation of service quality across the globe." Tourism Management 75, no. : 491-508.
This study examines the spatiotemporal dependency between traffic links. We model the traffic flow of 140 traffic links in a sub-network of the Minneapolis-St. Paul highway system for both rush hour and non-rush hour time intervals, and validate the extracted network weight matrix. The results of the modeling indicate: (1) the spatial weight matrix is unstable over time-of-day, while the network weight matrix is robust in all cases and (2) the performance of the network weight matrix in non-rush hour traffic regimes is significantly better than rush hour traffic regimes. The results of the validation show the network weight matrix outperforms the traditional way of capturing spatial dependency between traffic links. Averaging over all traffic links and time, this superiority is about 13.2% in rush hour and 15.3% in non-rush hour, when only the first-order neighboring links are embedded in modeling. In addition, this study proposes a two-step algorithm to search and identify the best look-back time window for upstream links. We indicate the best look-back time window depends on the travel time between two study detectors, and it varies by time-of-day and traffic link.
Alireza Ermagun; David Levinson. Spatiotemporal short-term traffic forecasting using the network weight matrix and systematic detrending. Transportation Research Part C: Emerging Technologies 2019, 104, 38 -52.
AMA StyleAlireza Ermagun, David Levinson. Spatiotemporal short-term traffic forecasting using the network weight matrix and systematic detrending. Transportation Research Part C: Emerging Technologies. 2019; 104 ():38-52.
Chicago/Turabian StyleAlireza Ermagun; David Levinson. 2019. "Spatiotemporal short-term traffic forecasting using the network weight matrix and systematic detrending." Transportation Research Part C: Emerging Technologies 104, no. : 38-52.
This study explores the cycling usage and frequency determinants in college campuses located in the Baltimore Metropolitan Area. The study discerns the attitudes of individuals toward the proposed infrastructure and environmental improvements with the goal of promoting biking to campus. We develop a structural equation model (SEM) using the travel information of 780 individuals, which was collected between December 2014 and June 2015. The results indicate risk factors have a higher explanatory value on bike-to-campus frequency than campus infrastructure and program. We further examine how and to what extent mixed populations on college campuses respond to latent factors. The findings pinpoint that males are less concerned about the risk-related indicators such as theft and road and environment-related obstacles such as poor road conditions. However, females have a positive attitude toward campus-related improvements such as pro-bike programs. Overall, students show a negative attitude toward the road and environmentally-related obstacles compared to staff and faculty. Minority groups, specifically African American and Asian, show a positive attitude toward campus-related improvements, unlike white participants. The findings can assist planners and advocates in implementing effective policy measures to increase bike-to-campus frequency.
Kaveh Bakhsh Kelarestaghi; Alireza Ermagun; Kevin P. Heaslip. Cycling usage and frequency determinants in college campuses. Cities 2019, 90, 216 -228.
AMA StyleKaveh Bakhsh Kelarestaghi, Alireza Ermagun, Kevin P. Heaslip. Cycling usage and frequency determinants in college campuses. Cities. 2019; 90 ():216-228.
Chicago/Turabian StyleKaveh Bakhsh Kelarestaghi; Alireza Ermagun; Kevin P. Heaslip. 2019. "Cycling usage and frequency determinants in college campuses." Cities 90, no. : 216-228.
The use of social media is increasingly considered a state-of-the-art practice for marketing campaigns. Researchers have investigated various social networks and media platforms to capture the behavior and characteristics of customers. This paper analyzes the official Twitter account of an airline company, Air New Zealand, to explore its market segments. To detect the communities of customers, we develop a network clustering method, which reveals the community classes of the network, along with a text mining analysis on each community detected by the cluster analysis. The results of the network analysis demonstrate that the social network of customers in the airline industry follows Pareto's principle that is similar to scale free networks. The findings of network clustering indicate Air New Zealand is essentially followed by New Zealand citizens. The local accounts are categorized into four communities: (1) lambda New Zealand citizens, (2) management, marketing, and digital media companies, (3) tourism and dining sectors, and (4) New Zealand sport players; while the global accounts fall into two communities: (1) worldwide celebrities and (2) the travel and aviation industry. The community detection method developed in this research is beneficial for marketing and customer strategy purposes as it enables airline companies to detect the categories of passengers interested into the brand. It also allows them to identify the potential sources for advertising by seeking out exceptionally connected customers who have high degrees of centrality.
Aymeric Punel; Alireza Ermagun. Using Twitter network to detect market segments in the airline industry. Journal of Air Transport Management 2018, 73, 67 -76.
AMA StyleAymeric Punel, Alireza Ermagun. Using Twitter network to detect market segments in the airline industry. Journal of Air Transport Management. 2018; 73 ():67-76.
Chicago/Turabian StyleAymeric Punel; Alireza Ermagun. 2018. "Using Twitter network to detect market segments in the airline industry." Journal of Air Transport Management 73, no. : 67-76.