Modeling Predictability of Traffic Counts at Signalised Intersections Using Hurst Exponent
Predictability is important in decision-making in many fields, including transport. The ill-predictability of time-varying processes poses severe problems for traffic and transport planners. The sources of ill-predictability in traffic phenomena could be due to uncertainty and incompleteness of data...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/2/188 |
| _version_ | 1797415615770656768 |
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| author | Sai Chand |
| author_facet | Sai Chand |
| author_sort | Sai Chand |
| collection | DOAJ |
| description | Predictability is important in decision-making in many fields, including transport. The ill-predictability of time-varying processes poses severe problems for traffic and transport planners. The sources of ill-predictability in traffic phenomena could be due to uncertainty and incompleteness of data and models and/or due to the complexity of the processes itself. Traffic counts at intersections are typically consistent and repetitive on the one hand and yet can be less predictable on the other hand, in which on any given time, unusual circumstances such as crashes and adverse weather can dramatically change the traffic condition. Understanding the various causes of high/low predictability in traffic counts is essential for better predictions and the choice of prediction methods. Here, we utilise the Hurst exponent metric from the fractal theory to quantify fluctuations and evaluate the predictability of intersection approach volumes. Data collected from 37 intersections in Sydney, Australia for one year are used. Further, we develop a random-effects linear regression model to quantify the effect of factors such as the day of the week, special event days, public holidays, rainfall, temperature, bus stops, and parking lanes on the predictability of traffic counts. We find that the theoretical predictability of traffic counts at signalised intersections is upwards of 0.80 (i.e., 80%) for most of the days, and the predictability is strongly associated with the day of the week. Public holidays, special event days, and weekends are better predictable than typical weekdays. Rainfall decreases predictability, and intersections with more parking spaces are highly predictable. |
| first_indexed | 2024-03-09T05:51:14Z |
| format | Article |
| id | doaj.art-53072209e65d402bae9b89d2f743dcc8 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T05:51:14Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-53072209e65d402bae9b89d2f743dcc82023-12-03T12:17:17ZengMDPI AGEntropy1099-43002021-02-0123218810.3390/e23020188Modeling Predictability of Traffic Counts at Signalised Intersections Using Hurst ExponentSai Chand0Research Centre for Integrated Transport Innovation (rCITI), School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, AustraliaPredictability is important in decision-making in many fields, including transport. The ill-predictability of time-varying processes poses severe problems for traffic and transport planners. The sources of ill-predictability in traffic phenomena could be due to uncertainty and incompleteness of data and models and/or due to the complexity of the processes itself. Traffic counts at intersections are typically consistent and repetitive on the one hand and yet can be less predictable on the other hand, in which on any given time, unusual circumstances such as crashes and adverse weather can dramatically change the traffic condition. Understanding the various causes of high/low predictability in traffic counts is essential for better predictions and the choice of prediction methods. Here, we utilise the Hurst exponent metric from the fractal theory to quantify fluctuations and evaluate the predictability of intersection approach volumes. Data collected from 37 intersections in Sydney, Australia for one year are used. Further, we develop a random-effects linear regression model to quantify the effect of factors such as the day of the week, special event days, public holidays, rainfall, temperature, bus stops, and parking lanes on the predictability of traffic counts. We find that the theoretical predictability of traffic counts at signalised intersections is upwards of 0.80 (i.e., 80%) for most of the days, and the predictability is strongly associated with the day of the week. Public holidays, special event days, and weekends are better predictable than typical weekdays. Rainfall decreases predictability, and intersections with more parking spaces are highly predictable.https://www.mdpi.com/1099-4300/23/2/188intersectionstraffic countpredictabilityHurst exponent |
| spellingShingle | Sai Chand Modeling Predictability of Traffic Counts at Signalised Intersections Using Hurst Exponent Entropy intersections traffic count predictability Hurst exponent |
| title | Modeling Predictability of Traffic Counts at Signalised Intersections Using Hurst Exponent |
| title_full | Modeling Predictability of Traffic Counts at Signalised Intersections Using Hurst Exponent |
| title_fullStr | Modeling Predictability of Traffic Counts at Signalised Intersections Using Hurst Exponent |
| title_full_unstemmed | Modeling Predictability of Traffic Counts at Signalised Intersections Using Hurst Exponent |
| title_short | Modeling Predictability of Traffic Counts at Signalised Intersections Using Hurst Exponent |
| title_sort | modeling predictability of traffic counts at signalised intersections using hurst exponent |
| topic | intersections traffic count predictability Hurst exponent |
| url | https://www.mdpi.com/1099-4300/23/2/188 |
| work_keys_str_mv | AT saichand modelingpredictabilityoftrafficcountsatsignalisedintersectionsusinghurstexponent |