Browsing by Author "Huang, Zhiren"

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Estimating inter-regional mobility during disruption: Comparing and combining different data sources
(2023-04) Heydari, Sara; Huang, Zhiren; Hiraoka, Takayuki; Ponce de Leon Chavez, Alejandro; Ala-Nissila, Tapio; Leskelä, Lasse; Kivelä, Mikko; Saramäki, Jari
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
A quantitative understanding of people’s mobility patterns is crucial for many applications. However, it is difficult to accurately estimate mobility, in particular during disruption such as the onset of the COVID-19 pandemic. Here, we investigate the use of multiple sources of data from mobile phones, road traffic sensors, and companies such as Google and Facebook in modelling mobility patterns, with the aim of estimating mobility flows in Finland in early 2020, before and during the disruption induced by the pandemic. We find that the highest accuracy is provided by a model that combines a past baseline from mobile phone data with up-to-date road traffic data, followed by the radiation and gravity models similarly augmented with traffic data. Our results highlight the usefulness of publicly available road traffic data in mobility modelling and, in general, pave the way for a data fusion approach to estimating mobility flows.
Evaluation Framework for Multi-Modal Public Transport Systems Based on Connectivity and Transfers at Stop Level
(2024-10) Sipetas, Charalampos (Haris); Huang, Zhiren; Espinosa Mireles de Villafranca, Alonso
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Multi-modal public transport (PT) networks within metropolitan areas are often characterized by complexity resulting mainly from their infrastructure, design, operations, and demand. This complexity leads to a significant amount of effort on behalf of the transit agencies to properly evaluate their performance at certain locations and proceed with improvements. This study proposes a methodology based on clustering techniques that facilitates the evaluation of PT networks. The evaluation framework refers to the comparison between the levels of supply and demand at a certain stop. Service supply is quantified through an existing connectivity index, whereas demand is considered through the number of transfers that are performed at each stop. Transfers are critical within multi-modal mobility and often serve as a hindrance for choosing PT. The case study here is the Helsinki PT network in Finland. General Transit Feed Specification (GTFS) data are used for quantifying connectivity and a dataset deriving from smartphone ticketing application for quantifying transfers. Results include the evaluation for each PT mode and for the overall multi-modal PT network. Focusing on the evaluation of the overall multi-modal PT network, connectivity and transfers levels for 75.60% of stops are found to be well aligned. Therefore, these stops could be eliminated from the list of candidate stops for performing improvements. Of the remaining stops, 19.73% belongs to the case of higher connectivity than transfers and 4.67% to the case of lower connectivity than transfers. Stops included in these two cases require further attention and prioritization during planning processes.
Evolution of public transport networks of 14 Finnish cities: efficiency, navigability, and regional differences
(2024-09-26) Niskanen, Anni-Mari
School of Science | Master's thesis
A functional and efficient public transport system is essential for any modern city. This Master's thesis studies the public transport systems of 14 Finnish cities based on public transport timetable data. The systems and their changes were studied by inspecting and comparing two instances of each city's system, one from 2018 and another from 2024. Both the efficiency and navigability of the systems were examined. Furthermore, travelling to the public hospitals and health stations of the cities, critical locations that a public transport user might have to reach quickly and navigate easily to in an emergency, was inspected separately. It was also of interest to find groups of cities with similar public transport systems. The public transport systems of the cities were studied by presenting them as public transport networks (PTNs) and calculating a multitude of measures that describe the systems from the PTNs. The PTNs were modelled both as static and temporal PTNs, which omitted and included the PTNs' evolution in time, respectively. The PTNs were also clustered with hierarchical clustering by utilising some of the calculated measures. The results of this thesis show that most of the inspected PTNs rely mainly on routes that pass through the city centre. Moreover, almost all of the cities have invested in their PTNs to different extents between 2018 and 2024. Two PTNs have undergone overhauls whose effects are twofold, as some areas of the cities have benefitted from them while others have suffered. Conversely, the overhaul of one of the PTNs that has made it reliant on routes that pass through the city centre has mainly improved the PTN. Furthermore, some of the cities have heavily invested in their PTNs and made them more efficient overall, but at the cost of deteriorated navigability. Conversely, some of the other cities have made cuts to their PTNs, which has made the PTNs less efficient but more easily navigable in some areas of the cities. Additionally, it was discovered that most of the cities have neither particularly improved nor neglected the travelling to their public hospitals and health stations. Finally, two groupings of the PTNs were discovered, the first grouping separating the PTNs of large and small cities of Finland and the second separating the PTNs of cities in western and eastern Finland.
Expressway Usage Pattern Analysis Based on Tollgate Data: A Case Study of the Shandong Province, China
(2023-09-29) Wang, Chengcheng; Ding, Xiaoe; Wang, Chao; Lv, Mengqi; Xu, Run; Bi, Yufeng; Huang, Zhiren
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Expressway transportation is an essential part of regional development. An efficient expressway system can enhance cities’ connectivity and coordinate long-distance trips between urban areas. Understanding how travel demand affects the flow of expressways is crucial to designing an efficient expressway management system. However, congested expressways are substantial obstacles to unimpeded expressway travel. Here, we explore the relationship between demand origin locations and congested expressways. We extract the time-varying OD demand matrix from empirical tollgate data collected in Shandong province, China. The incremental traffic assignment method is introduced to obtain the traffic flow of expressway road segments. It was found that congested expressways were generated due to only a few origin locations. In addition, expressway congestion during peak hours could be effectively alleviated by controlling the travel demand of these origin locations. Therefore, the proposed method can provide a novel perspective for expressway management.
Mobility Signatures: A Tool for Characterizing Cities Using Intercity Mobility Flows
(2022-02-24) Kiashemshaki, Maryam; Huang, Zhiren; Saramäki, Jari
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Understanding the patterns of human mobility between cities has various applications from transport engineering to spatial modeling of the spreading of contagious diseases. We adopt a city-centric, data-driven perspective to quantify such patterns and introduce the mobility signature as a tool for understanding how a city (or a region) is embedded in the wider mobility network. We demonstrate the potential of the mobility signature approach through two applications that build on mobile-phone-based data from Finland. First, we use mobility signatures to show that the well-known radiation model is more accurate for mobility flows associated with larger Finnish cities, while the traditional gravity model appears a better fit for less populated areas. Second, we illustrate how the SARS-CoV-2 pandemic disrupted the mobility patterns in Finland in the spring of 2020. These two cases demonstrate the ability of the mobility signatures to quickly capture features of mobility flows that are harder to extract using moretraditional methods.
A Two-Step Model for Predicting Travel Demand in Expanding Subways
(2022-10-01) Wang, Kaipeng; Wang, Pu; Huang, Zhiren; Ling, Ximan; Zhang, Fan; Chen, Anthony
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
In many cities, subways are expanding with new or extended lines being built and put into operations. The prediction of future travel demand in subway with the planned expansion is of significant importance because such information is crucial for new line planning and new network operations. In this study, we identify the determinant features from potential influential factors of passenger travel demand and develop a two-step model for predicting passenger travel demand in expanding subways. The proposed model is tested in an actual subway with a new line being put into operations, and achieves higher prediction accuracy than the benchmark models.
Uncovering the spatial connection of human mobility based on egocentric networks
(2021-08-23) Kiashemshaki, Maryam
Perustieteiden korkeakoulu | Master's thesis
Understanding human mobility patterns has various applications in urban planning, traffic engineering, infectious disease spreading, and emergency management. Temporal characters of mobility networks can be extracted from origin-destination matrices containing complete information on commuting ﬂows. However, analyzing and comparing individuals' daily mobility are not easy tasks. Therefore, it is crucial to find patterns in human mobility to extract helpful information from enormous spatial data. This study aims to cluster cities with similar mobility patterns to discover other similar features for these cities. To achieve this aim, the distribution of destinations of outgoing travelers from a given city and the persistence of these patterns over time are studied. These patterns may be unique for each city; therefore, they are named city signatures. The concept of signature is given from the egocentric network studies, in which the structures of social networks are investigated by studying the communication patterns of individuals. Then, the introduced concept is applied to analyze origin-destination matrices extracted from two anonymized mobile phone datasets (Spanish and Finnish). The city signatures are examined from two perspectives: 1) the difference of cities' signatures from each other, 2) the variation of city signatures in the different periods. For this purpose, distribution characteristics, such as the radius of gyration, entropy, and Gini index, are measured. The results further reveal that the Finnish human mobility patterns are different from the Spanish ones. The city signature in Spain is flatter, while in Finland, it is steeper. This means that in Spain people might travel from any given city to another, whereas in Finland certain cities have more travel between them than others. Additionally, the analysis in the Finnish dataset demonstrates how COVID-19 affects human mobility. The result shows that although human mobility patterns do not noticeably change before and during the COVID-19 pandemic, the average distance of trips has reduced. Therefore, it can be concluded that during the COVID-19, individuals have traveled less, and if they traveled, they would go to their local area.
Understanding individual and collective human mobility patterns in twelve crowding events occurred in Shenzhen
(2022-06) Guo, Bao; Yang, Hu; Zhou, Hui; Huang, Zhiren; Zhang, Fan; Xiao, Longwen; Wang, Pu
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Crowding events, which pose tremendous pressure to city management and society safety, are a typical manifestation of anomalous human mobility in metropolitan areas. However, we are still lacking a comprehensive understanding of the anomalous human mobility in crowding events, which is crucial for preventing crowd disasters and developing sustainable cities and societies. In this study, we analyze the individual and collective human mobility patterns in crowding events using the smart card data of six million subway passengers in Shenzhen city. The discovered individual human mobility patterns reveal the underlying mechanism of crowd formation. The discovered collective human mobility patterns can be employed to anticipate crowding events, offering timely information for transportation and crowd management.
Understanding the predictability of path flow distribution in urban road networks using an information entropy approach
(2024-06) Guo, Bao; Huang, Zhiren; Zheng, Zhihao; Zhang, Fan; Wang, Pu
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Predicting the distributions of path flow between origin-destination (OD) pairs in an urban road network is crucial for developing efficient traffic control and management strategies. Here, we use the large-scale taxi GPS trajectory data of San Francisco and Shenzhen to study the predictability of path flow distribution in urban road networks. We develop an approach to project the time-varying path flow distributions into a high-dimensional space. In the high-dimensional space, information entropy is used to measure the predictability of path flow distribution. We find that the distributions of path flow between OD pairs are in general characterized with a high predictability. In addition, we analyze the factors affecting the predictability of path flow distribution. Finally, an n-gram model incorporating high-order gram and low-order gram is proposed to predict the distribution of path flow. A relatively high prediction accuracy is achieved.
Using temporal public transport demand profiles to reveal urban spatial patterns
(2024) Espinosa Mireles de Villafranca, Alonso; Huang, Zhiren; Sipetas, Charalampos (Haris)
A4 Artikkeli konferenssijulkaisussa
We present a versatile method, inspired by computational neuroscience, for reconstructing smooth demand profiles from sparse timestamp data for public transport boardings. We show how areas can be clustered based on the similarity of their temporal demand profiles to reveal urban spatial patterns. We use the Helsinki metropolitan region to showcase the method using data on boarding events from the TravelSense data from HSL (the Helsinki region transport authority) collected through their mobile ticketing app. Our results show the show the dependence of travel demand on available public transit and modes and supply volume. Furthermore, the clusters align with extremely well with the types of urban areas in the region. Due to the high supply and even frequency of transit options, the differences in demand profiles are due to mode availability and land-use features rather than frequency patterns.
Which version of betweenness centrality is the best predictor of traffic volume?
(2023-12-12) Westerback, Liva
Perustieteiden korkeakoulu | Master's thesis
The traffic volume at specific road segments is often quantified by the Annual Average Daily Traffic (AADT). In network science and space syntax, traffic is modelled as flows that take place on road networks. It is conjectured that one can estimate AADT by a network-theoretical measure called betweenness centrality (BC), or shortest-path BC, which measures on how many of all shortest paths in the network a given node or edge lies. It holds when all agents choose the shortest path and the demand between all node pairs is uniform. On a real-world road network, however, BC does not predict AADT very well. Researchers have taken multiple approaches to modify BC into a better predictor, but there is no agreement on how to do it best. Hence, I aim to compare different approaches and find out which version of BC is the best predictor of traffic volume. I create various road networks of Greater London and five metropolitan counties in England, from historical OpenStreetMap data from both 2019 and 2021, and calculate multiple versions of BC on them. Then, I study the correlations between the calculated centralities and observed traffic count data from the United Kingdom from 2019 and 2021. I find that the largest improvements on traditional BC are obtained by using travel time as edge weight and by creating the network with a buffer zone. The very best traffic predictor of the studied measures is travel-time-weighted shortest-path BC with a 30-minute cutoff calculated on a primal network representation with a 30-minute buffer zone. These results answer the long-standing question of how to modify BC to best predict AADT. The predictive performance of my best measure is significant in the research field, particularly for a model that requires minimal data collection, and can be very valuable for road network planning.