Daily Traffic Patterns Research Articles

Characterization of equivalent Black Carbon (eBC) in different environments in the western Mediterranean

This work is focused on the characterization of equivalent Black Carbon (eBC) mass concentrations at two sites (suburban and rural background) in Valencia, Spain. The suburban station is in the metropolitan area of Valencia (the third largest in Spain), so the analysis of eBC is crucial to determine its impact on urban pollution. The mean (±standard deviation) concentration from April 2011 to March 2023 was 1.0 ± 0.5 μgm−3, ranging from 0.7 μgm−3 in summer to 1.4 μgm−3 in winter. The seasonality is related to the intraannual variability of the Boundary Layer Height (BLH) and the daily variation is closely related to the daily traffic pattern, especially to rush hours. Applying the Mann–Kendall test, eBC levels exhibited decreasing trends for all seasons, showing slopes between −15%/yr and −5%/yr. In addition, the Absorption Å ngström Exponent shows a significant upward trend for all periods analyzed.The rural station is in a remote site at 1280 m.a.s.l., 80 km from the suburban station. The overall mean from 2017 to 2023 (± standard deviation) was 0.17 ± 0.14 μgm−3, ranging from 0.08 μgm−3 in winter to 0.20 μgm−3 in summer. This seasonality is due to a combined effect of local dynamics of the BLH and the station altitude. The daily variability is smoother, without marked changes. The maximum daily concentration is reached in the late afternoon. According to the Conditional Bivariate Probability Function (CBPF) diagrams, the transport from the city of Valencia (SE direction) is the most relevant contribution at the measurement location.

On clustering coherent optics point-to-multipoint trees for cost-effective bandwidth assignment in MANs

Point-to-multipoint (P2MP) coherent pluggable transceivers leveraging digital subcarrier multiplexing (DSCM) techniques are gaining momentum among telecommunication operators as a technology that can potentially provide broadband connectivity in metropolitan area networks (MANs) with flexible bandwidth allocation at reduced costs. This technology allows trees to be built where central metro (hub) nodes are interconnected with remote access/aggregation (spoke) nodes with dedicated and flexible bandwidth slices, enabling better resource management, simplified architectures, and reduced costs. This paper proposes a modified version of AI/ML clustering techniques that concentrates on the same P2MP tree as those low-rate leaf nodes with uncorrelated daily traffic patterns in a cost-effective manner, thus allowing 10%–18% tree reductions. This, when compared with fixed P2P transceivers, translates into potential CAPEX savings between 40% and 50% in the different scenarios here considered.

Modeling and Prediction of Daily Traffic Patterns—WASK and SIX Case Study

The paper studies efficient modeling and prediction of daily traffic patterns in transport telecommunication networks. The investigation is carried out using two historical datasets, namely WASK and SIX, which collect flows from edge nodes of two networks of different size. WASK is a novel dataset introduced and analyzed for the first time in this paper, while SIX is a well-known source of network flows. For the considered datasets, the paper proposes traffic modeling and prediction methods. For traffic modeling, the Fourier Transform is applied. For traffic prediction, two approaches are proposed—modeling-based (the forecasting model is generated based on historical traffic models) and machine learning-based (network traffic is handled as a data stream where chunk-based regression methods are applied for forecasting). Then, extensive simulations are performed to verify efficiency of the approaches and their comparison. The proposed modeling method revealed high efficiency especially for the SIX dataset, where the average error was lower than 0.1%. The efficiency of two forecasting approaches differs with datasets–modeling-based methods achieved lower errors for SIX while machine learning-based for WASK. The average prediction error for SIX reached 3.36% while forecasting for WASK turned out extremely challenging.

Within-day travel speed pattern unsupervised classification – A data driven case study of the State of Alabama during the COVID-19 pandemic

Recent comparative studies on mobility patterns are emerging to describe the changes in mobility patterns due to the COVID-19 pandemic. Most of the current studies utilize travel volume per day as the critical indicator and identify the impacted period by the dates of governmental lockdown or stay-at-home orders, which however may not accurately present the actual impacted dates. The objective of this study is to provide an alternative perspective to identify the normal and pandemic-influenced daily traffic patterns. Instead of only using traffic volumes per day or assuming the impacted travel pattern began with the stay-at-home order, the methodology in this study investigates the within-day time-dependent travel speed as time series, and then applies dynamic time warping algorithm and hierarchical clustering unsupervised classification methods to classify days into various groups without assuming a start date for any group. Using the state-wide travel speed data in Alabama, these study measures dissimilarities among within-day travel speed time series. By incorporating the dissimilarities/distance matrix, various agglomerative hierarchical clustering (AHC) methods (average, complete, Ward's) are tested to conduct proper unsupervised classification. The Ward's AHC classification results show that within-day travel speed pattern in Alabama shifted more than two weeks before the issuance of the State stay-at-home order. The results further show that a new travel speed pattern appears at the end of stay-at-home order, which is different from either the normal pattern before the pandemic or the initial pandemic-influenced pattern, which leads to a conclusion that a ‘new normal’ within-day travel pattern emerges. • An alternative perspective to identify the normal and pandemic-influenced daily traffic patterns. • The within-day time-dependent travel speed was investigated as time series. • Dynamic time warping and hierarchical clustering were used to classify days into various groups. • The Ward's AHC classification showed within-day travel speed pattern change in Alabama two weeks before the lockdown.

Reducing the Energy Footprint of Cellular Networks with Delay-Tolerant Users

One of the most promising techniques to drastically reduce the energy consumption of cellular networks is the use of sleep-mode (SM) methods: when the traffic load is low, some components of the network, such as a base station (BS), can be switched off. In this case, the traffic load is managed by BSs that stay on . In this paper, we investigate how user cooperation can further reduce the energy consumption of a cellular network that uses SM strategies. In particular, we study how sleeping periods can be extended when users tolerate a delay before the start of their service. We propose two delay-tolerant-user-aware SM strategies and provide mathematical grounds for the evaluation of their performance. We evaluate the strategies in the context of LTE networks with realistic daily traffic patterns. The results show considerable daily energy reductions (up to 21% compared to the always- on paradigm and up to 15% compared to the SM strategy without user cooperation).

An Approach to Dynamical Classification of Daily Traffic Patterns

AbstractThis article proposes a prototype of an urban traffic control system based on a prediction‐after‐classification approach. In an off‐line phase, a repository of traffic control strategies for a set of (dynamic) traffic patterns is constructed. The core of this stage is the k‐means algorithm for daily traffic pattern identification. The clustering method uses the input attributes flow, speed, and occupancy and it transforms the dynamic traffic data at network level in a pseudo‐covariance matrix, which collects the dynamic correlations between the road links. A desirable number of traffic patterns is provided by Bayesian Information Criterion and the ratio of change in dispersion measurements. In an on‐line phase, the current daily traffic pattern is predicted within the repository and its associated control strategy is implemented in the traffic network. The dynamic prediction scheme is constructed on the basis of an existing static prediction method by accumulating the trials on set of patterns in the repository. This proposal has been assessed in synthetic and real networks testing its effectiveness as a data mining tool for the analysis of traffic patterns. The approach promises to effectively detect the current daily traffic pattern and is open to being used in intelligent traffic management systems.

Efficient Logical Topology Design Considering Multiperiod Traffic in IP-over-WDM Networks

In recent years energy consumption has become a main concern for network development, due to the exponential increase of network traffic. Potential energy savings can be obtained from a load-adaptive scheme, in which a day can be divided into multiple time periods according to the variation of daily traffic patterns. The energy consumption of the network can be reduced by selectively turning off network components during the time periods with light traffic. However, the time segmentation of daily traffic patterns affects the energy savings when designing multiperiod logical topology in optical wavelength routed networks. In addition, turning network components on or off may increase the overhead of logical topology reconfiguration (LTR). In this paper, we propose two mixed integer linear programming (MILP) models to design the optimal logical topology for multiple periods in IP-over-WDM networks. First, we formulate the time-segmentation problem as an MILP model to optimally determine the boundaries for each period, with the objective to minimize total network energy consumption. Second, another MILP formulation is proposed to minimize both the overall power consumption (PC) and the reconfiguration overhead (RO). The proposed models are evaluated and compared to conventional schemes, in view of PC and RO, through case studies.

Traffic Management as a Service: The Traffic Flow Pattern Classification Problem

Intelligent Transportation System (ITS) technologies can be implemented to reduce both fuel consumption and the associated emission of greenhouse gases. However, such systems require intelligent and effective route planning solutions to reduce travel time and promote stable traveling speeds. To achieve such goal these systems should account for both estimated and real-time traffic congestion states, but obtaining reliable traffic congestion estimations for all the streets/avenues in a city for the different times of the day, for every day in a year, is a complex task. Modeling such a tremendous amount of data can be time-consuming and, additionally, centralized computation of optimal routes based on such time-dependencies has very high data processing requirements. In this paper we approach this problem through a heuristic to considerably reduce the modeling effort while maintaining the benefits of time-dependent traffic congestion modeling. In particular, we propose grouping streets by taking into account real traces describing the daily traffic pattern. The effectiveness of this heuristic is assessed for the city of Valencia, Spain, and the results obtained show that it is possible to reduce the required number of daily traffic flow patterns by a factor of 4210 while maintaining the essence of time-dependent modeling requirements.

EANN 2012: exploratory analysis of mobile phone traffic patterns using 1-dimensional SOM, clustering and anomaly detection

Mobile network is a continuously evolving system. Recognizing the behaviour of the subscribers helps the operator in managing and developing the services they offer. Valuable information for those purposes can be revealed by analyzing the daily traffic patterns which result from the usage habits of the subscribers. In this paper we show how exploratory data analysis can be used to discover valuable information from the daily patterns. We present generic tools and methods that are especially suitable for data that consists of patterns. The proposed procedure consists of four parts: preprocessing, day of the week analysis, network element analysis and anomaly detection. The methods are based on clustering and one dimensional self organizing map. One dimensional map enables compact visualization that is especially suitable for data where the variables are not independent but form a pattern. We found out that the day of the week is the main explanation for the traffic patterns on weekends. On weekdays the traffic patterns are mostly specific to groups of networks elements, not the day of the week. The results on the mobile network traffic are presented on a general level. Detailed analysis of the root causes behind the findings requires knowledge about the configuration, topology and geography of the network, which are available only for the operator.

Integrated daily commuting patterns and optimal road tolls and parking fees in a linear city

For decades, the dynamic traffic patterns of morning and evening commutes have been investigated separately, and it is often assumed that they are simple mirror symmetries. In this paper, we use a two-stage differential method to establish a daily traffic pattern that links the morning and evening commutes as an integrated one. Based on a bi-direction bottleneck network with a spatial pattern of parking, we use analytical models to describe travelers’ behavior in choosing departure times in their morning and evening trips, where a commuter’s morning and evening decisions are joined by a parking location. Given fixed parking locations of commuters, we firstly derive the evening commute pattern, which is a Nash equilibrium in the sense that no one can reduce her/his travel cost given other commuters’ decisions. Then the individual evening commute costs are allocated to different parking locations in modeling the morning commuting behavior, and the morning travel pattern is a user equilibrium in the sense that everyone has equal daily travel cost and no one can reduce private travel cost by unilaterally changing travel decisions. Then we propose a time-varying road toll regime to eliminate queuing delay and reduce schedule delay penalty. Furthermore, a time-varying road toll and location-dependent parking fee regime is developed to achieve a system optimum where the morning schedule delay cost is further reduced to the minimum by reversing the spatial order of parking. In view of the fact that road pricing is hard to implement, we propose a location-dependent parking fee regime with no road tolls to optimize the morning commute pattern, without improving the evening commute pattern.

Traffic and seasonal dependence of the light absorption coefficient in Santiago de Chile

We have designed and built a compact, low-cost automated system to measure the optical absorption coefficient of air. Because most of the light absorption is due to black carbon, this method is a direct measure of the amount of black carbon in the atmosphere. The equipment was used to measure absorption over a period of one year in a central area of Santiago. Our results show a strong correlation with the daily traffic pattern. The highest value of the absorption coefficient during most of the year occurs during the morning rush hour (0700-0800), and the lowest value either early in the morning (0300-0500) or in the afternoon (1400-1700). The absorption coefficient also shows a strong dependence with the season of the year, with values 10-20 times higher in winter than in summer. The data show that, during most of the year, the amount of black carbon present in the atmosphere is due to traffic. At night, during winter, the high concentration of black carbon is due to the temperature inversion effect.

A simulation-based assessment of route guidance benefits under variable network congestion conditions

The purpose of this study is to establish a quantitative relationship between network congestion and travel-time reduction benefits of a real-time route guidance user service. The approach of the study is to employ the INTEGRATION traffic simulation model and a 2,000 link network based on the Detroit, Michigan roadway system in a series of experiments. While holding the capacity of the roadway fixed, the value of route guidance is evaluated over a range of increasing demand levels. Network demand patterns and trip characteristics are comparable to current national averages. Measures of congestion such as average system commute speed either match or exceed current national averages. Congestion metrics measured for the lightest demand scenario match most current empirical national average data, while the heaviest demand scenario appears roughly comparable with 1994 Tokyo conditions. Results from this study indicate that route-guided vehicles benefit regardless of level of congestion, however, the amount of trip time savings achieved is highly dependent on network congestion conditions. Average benefits for route guided vehicles over unguided vehicles in the A.M. peak period range between 8–26% depending on overall traffic volume. The results indicate a two-part linear relationship between route guidance benefit and network congestion. As congestion increases in the network, benefits of route guidance increase until average network speed drops below 20mph. Beyond that point, benefits decline (but remain positive). This 20mph threshold in our network is the point where the dynamically growing and shifting mass of queued vehicles around bottlenecks begins to impede access to alternative routes for guided vehicles network-wide. In a related experiment, route guided vehicles that receive reliable data on network conditions (including incidents or demand variation) gain 3–9% travel time savings over unguided vehicles that follow optimal routes based on average time-variant network congestion conditions. Route guided vehicles may exploit information about unexpected delays in the network related to incidents as well as variability in daily traffic patterns. This experiment was conducted to isolate the value of route guidance with respect to experienced commuter traffic, rather than an aggregated model of driver behavior including both familiar and unfamiliar drivers. The preliminary results of this study have implications for ITS benefit assessment. First, the benefits of route guidance are directly related to the level of recurrent congestion in a network. Thus, a near-term poor market for route guidance may evolve over time into a good market for these services. Likewise, a good market for a route guidance user service may deteriorate if overall network congestion reaches very high levels. Second, a route guidance user service provides benefits compared to both a model of aggregated unguided traveler behavior and a model of experienced commuter behavior, regardless of congestion levels. Third, route guided vehicles are demonstrated to gain benefit by avoiding the worst congestion in the network. This minimization in day-to-day variability in commute time may be the most significant benefit of the route guidance system for the familiar driver.