Travel Time Patterns Research Articles

Travel Time Reliability Analysis on Paratransit, case study: Substação-Muahivire route, Nampula - Mozambique

In numerous medium-sized cities in Sub-Saharan Africa, such as Nampula, the reliability of mini-bus travel time is frequently a subject of inquiry. One contributing factor to this uncertainty is the considerable number of stops made throughout the route for passenger-related purpose, including boarding and disembarking. This study utilizes data collected during off-peak periods to scrutinize the day-to-day travel time patterns of mini-buses along the route. The route has been segmented into intervals of 500 meters each, and the analysis focuses on the duration of trips at each segment. The evaluation is based on the Coefficient of Variation (CoV) of travel time, indicating the extent of dispersion in mini-bus travel time at the segment level. The results reveal that the CoVs at nearly all segments along the route are minimal, measuring less than 15%. This suggests that day-to-day variability of travel time at the segment level is negligible. However, on road segments characterized by commercial activities, the CoVs range from 15% to 22%.

Commercial floating car data application in Sub-Saharan African transport planning contexts: A critical review and research agenda

Floating car data (FCD) are a source of traffic information reported by GPS-enabled probes from within the traffic stream. FCD define speed, travel time and movement patterns, all key inputs for transport planning. FCD are readily available over the entire road network without roadside sensors and communications infrastructure, making this a particularly relevant traffic data source in low- and middle-income countries (LMICs) where resource limitations restrict the expansive distribution of traffic sensors. This review article aims to investigate the applicable use-cases of FCD in LMICs, while simultaneously sensitising transport practitioners to the potential data biases that may be present in FCD. Particularly, this paper considers whether the context of Sub-Saharan African countries allows for the application of FCD similarly to high-income country FCD use-cases, critically examining insights from literature. Sub-Saharan African countries typically differ from high-income nations with respect to urban form, population distribution (and segregation), modal choice, driver population, and technology penetration. To date, there has been no literature that concatenates the current practice of FCD use-cases in Sub-Saharan Africa, nor defines the fundamental differences between transport and land use planning in high- and low-income countries with the view to determine if these dissimilarities impact FCD collection and use. Literature revealed novel research involving the application of FCD to investigate informal public transport systems that are specific to Sub-Saharan Africa, while also determining that accuracy and potential biases of FCD are not widely understood in Sub-Saharan Africa. Findings show that biases are likely to occur in FCD in Sub-Saharan Africa because of the representation and behaviours of the sample reporting FCD, as well as the urban form of Sub-Saharan African cities. The paper concludes by developing a research agenda that will assist to further the knowledge of FCD sample bias and the applicable use-cases of FCD in Sub-Saharan Africa.

Effects of Data Characteristics on Bus Travel Time Prediction: A Systematic Study

The prediction of bus travel time with accuracy is a significant step toward improving the quality of public transportation. Drawing meaningful inferences from the data and using these to aid in prediction tasks is always an area of interest. Earlier studies predicted bus travel times by identifying significant regressors, which were identified based on chronological factors. However, travel time patterns may vary depending on time and location. A related question is whether the prediction accuracy can be improved with the choice of input variables. The present study analyzes this question systematically by presenting the input data in different ways to the prediction algorithm. The prediction accuracy increased when the dataset was grouped, and separate models were trained on them, the highest accurate case being the one where the data-derived clusters were considered. This demonstrates that understanding patterns and groups within the dataset helps in improving prediction accuracy.

Integrated ANN-Bayes-based travel time prediction modeling for signalized corridors with probe data acquisition paradigm

Travel time is a major information in support of Transportation System Management and Operations (TSMO). Accurate travel time information extracted from probe data sources, which have been evolved in recently years, has led to the considerable research into travel time prediction modeling on freeways. However, little research has been reported on dealing with predicting signalized arterial travel time. It is mainly because of the natures of interrupted traffic flows along a signalized corridor or dynamic urban network, wherein varying signal timing schemes, crossing traffic, and dynamic route choices make arterial travel time estimations much more challenging than freeways. This paper presents an innovative methodological framework that can be used to integrate the exponential smoothing technique, Artificial Neural Network (ANN) techniques and Bayes algorithms for predicting travel time along a signalized corridor. The data source obtained along the US-27 corridor in the State of Ohio, was used to test two types of the developed models, i.e., hourly instantaneous travel time prediction model and realized (or experienced) route travel time prediction model. The study results indicate that the developed models can effectively capture the travel time patterns by combining an exponential smoothing base profile and an ANN-Bayes-trained dynamic profile. In this way, more sensitive changes can be identified to a variation aroused by non-recurring congestion. The testing results with real-world travel time datasets indicates a good performance of the predicted travel time models. The most errors range from −0.608 to 0.485, which are much lower than the threshold standard deviation if 2.24 and close to 6% of mean travel time value of 9.08 min. Overall, the study results suggest that the proposed methods are capable of capturing traffic patterns even when an incident occurs by comparing with conventional methods.

Quantifying the Effect of Rainfall and Visibility Conditions on Road Traffic Travel Time Reliability

Abstract This research explores a data-driven methodological framework to quantify the effect of rainfall and visibility on travel time reliability (TTR) by considering selected road segments in North Carolina. The framework includes capturing, processing, and integrating weather-related information and travel time data for the selected road segments. Various TTR indices were computed for the selected road segments under different rainfall and visibility ranges by day of the week (DOW) and time of the day (TOD). The TTR indices were computed for one week before and after (same DOW and TOD) under the normal weather condition and compared with those obtained under different intensities of rainfall and visibility. The variability in travel time patterns due to other events is expected to be marginal when considering the same DOW and TOD for comparison purposes. The results indicate that poor visibility with different rainfall intensities has the maximum adverse effect on the TTR. The outcomes from the data-driven methodological framework help the transportation planners in developing weather-responsive traffic management strategies and assessing their effectiveness using TTR indices. Significance Statement Travel time reliability (TTR) generally refers to the level of consistency or dependability in transportation service. It is considered as a measure of road operational performance. Ensuring higher levels of reliability is critical for efficient transportation system management along with mobility and accessibility needs. However, factors such as weather condition have a negative effect on the TTR. A data-driven methodological framework is proposed by integrating weather information and travel time data to quantify the effect of common weather conditions like rainfall and visibility on the TTR. The results indicated that heavy rain and poor visibility have an adverse effect on the TTR. These results are useful for agencies to better manage the traffic under different weather conditions.

Seismic Diffraction Analysis of a Fluid Escape Pipe beneath the Submarine Gas Bubble Plume in the Haima Cold Seep Area

Pipe structures are considered as fluid conduits beneath cold seeps. These structures have been observed in many geological settings and are widely accepted as the most critical pathway for fluid migration. One of such pipe structures in the Haima cold seep region is investigated herein. The pipe structure extends from below the BSR and reaches the seafloor. It is characterized by a string of events with short and strong seismic amplitudes, similar to the string of bead reflections (SBRs) associated with small-scale caves in carbonate reservoirs. This leads to the hypothesis that multiple small-scale bodies exist within the pipe structure. We test this hypothesis by analysis of diffraction waves and numerical seismic modeling. Travel time pattern analysis indicates that the diffractors within the pipe structure caused the rich diffraction waves on the shot records, and the reversed polarity indicates that the diffractors have a lower impedance than the surrounding sediments. These low-impedance bodies are interpreted as gas pockets within the pipe structures. Based on these interpretations, a conceptual model is proposed to describe the fluid migration process within the pipe. Briefly, we propose that gas pockets within the pipe structure could be analogue to the magma chambers located beneath volcanoes and this may provide a new insight into how gases migrate through the pipe structure and reach the seafloor.

Statistical modelling of bus travel time with Burr distribution

A better understanding of the travel time distribution shape or pattern could improve the decision made by the transport operator to estimate the travel time required for the vehicle to travel from one place to another. Finding the most appropriate distribution to represent the day-to-day travel time variation of an individual link of a bus route is the main purpose of this study. Klang Valley, Malaysia is the study area for the research. A consecutive of 7 months ten bus routes automatic vehicle location (AVL) data are used to examine the distribution performance. The leading distribution proposed for the research is the Burr distribution. Both symmetrical and asymmetrical distributions that have been proposed in existing studies are also used for comparison purposes. Maximum likelihood estimation is applied for parameter estimation while loglikelihood value, Akaike information criterion (AIC) and Bayesian information criterion (BIC) are applied for performance assessment of the distributions. Promising results are obtained by the leading model in all different kinds of operating environment and could be treated as the preliminary preparation for further reliability analysis.

Bus Travel Time: Experimental Evidence and Forecasting

Bus travel time analysis plays a key role in transit operation planning, and methods are needed for investigating its variability and for forecasting need. Nowadays, telematics is opening up new opportunities, given that large datasets can be gathered through automated monitoring, and this topic can be studied in more depth with new experimental evidence. The paper proposes a time-series-based approach for travel time forecasting, and data from automated vehicle monitoring (AVM) of bus lines sharing the road lanes with other traffic in Rome (Italy) and Lviv (Ukraine) are used. The results show the goodness of such an approach for the analysis and reliable forecasts of bus travel times. The similarities and dissimilarities in terms of travel time patterns and city structure were also pointed out, showing the need to take them into account when developing forecasting methods.

A comparison in travel patterns and determinants of user demand between docked and dockless bike-sharing systems using multi-sourced data

The co-existence of traditional docked bike-sharing and emerging dockless systems presents new opportunities for sustainable transportation in cities all over the world, both serving door to door trips and accessing/egressing to/from public transport systems. However, most of the previous studies have separately examined the travel patterns of docked and dockless bike-sharing schemes, whereas the difference in travel patterns and the determinants of user demand for both systems have not been fully understood. To fill this gap, this study firstly compares the travel characteristics, including travel distance, travel time, usage frequency and spatio-temporal travel patterns by exploring the smart card data from a docked bike-sharing scheme and trip origin–destination (OD) data from a dockless bike-sharing scheme in the city of Nanjing, China over the same spatio-temporal dimension. Next, this study examines the influence of the bike-sharing fleets, socio-demographic factors and land use factors on user demand of both bike-sharing systems using multi-sourced data (e.g., trip OD information, smart card, survey, land use information, and housing prices data). To this end, geographically and temporally weighted regression (GTWR) models are built to examine the determinants of user demand over space and time. Comparative analysis shows that dockless bike-sharing systems have a shorter average travel distance and travel time, but a higher use frequency and hourly usage volume compared to docked bike-sharing systems. Trips of docked and dockless bike-sharing on workdays are more frequent than those on weekends, especially during the morning and evening rush hours. Significant differences in the spatial distribution between docked and dockless bike-sharing systems are observed in different city areas. The results of the GTWR models reveal that hourly docked bike-sharing trips and dockless bike-share trips influence each other throughout the week. The density of Entertainment points of interest (POIs) is positively correlated with the usage of dockless bike-sharing, but negatively correlated with docked bike-sharing usage. On the contrary, the proportion of the elderly has a positive association with the usage of docked bike-sharing, but a negative association with the usage of dockless bike-sharing. Finally, policy implications and suggestions are proposed to improve the performance of docked and dockless bike-sharing systems, such as increasing the flexibility of docked bike-sharing, designing and promoting mobile applications (APP) for docked bike-sharing, improving the quality of dockless shared bikes, and implementing dynamic time-based pricing strategies for dockless bike-sharing.

Prediction Of Trends In Bus Travel Time Using Spatial Patterns

Studying patterns in traffic data is a basic analysis to understand the system. In this study, a large amount of bus travel data collected using vehicle tracking devices is analyzed for patterns. Travel time, in general, follows both spatial and temporal patterns. Spatial patterns are expected because travel times in particular sections on a roadway can be following similar patterns. For example, sections with a bus stop in it may show similar patterns due to stopping at the bus stops. The present study explores the use of data-driven approaches, primarily clustering, to identify the spatial patterns in bus travel times. Discrete Wavelet Transform (DWT) is used to extract trends from the travel time measurements. Two popular clustering algorithms - k-means and hierarchical clustering algorithms are used in this study to identify the spatial patterns and group sections with similar characteristics. Once the spatial patterns are obtained, the historic database is searched to identify similar cluster patterns and travel time trends are predicted using Pattern Sequence-based Forecasting (PSF) algorithm. The performance of the proposed algorithm for the prediction of travel time trends of trips occurring during peak and off-peak hours of a day was then compared based on prediction errors.

Learning heterogeneous traffic patterns for travel time prediction of bus journeys

In this paper, we address the problem of travel time prediction of bus journeys which consist of bus riding times (may involve multiple bus services) and also the waiting times at transfer points. We propose a novel method called Traffic Pattern centric Segment Coalescing Framework (TP-SCF) that relies on learned disparate patterns of traffic conditions across different bus line segments for bus journey travel time prediction. Specifically, the proposed method consists of a training and a prediction stage. In the training stage, the bus lines are partitioned into bus line segments and the common travel time patterns of segments from different bus lines are explored using Non-negative Matrix Factorization (NMF). Bus line segments with similar patterns are classified into the same cluster. The clusters are then coalesced in order to extract data records for model training and bus journey time prediction. A separate Long Short Term Memory (LSTM) based model is trained for each cluster to predict the bus travel time under various traffic conditions. During prediction, a given bus journey is partitioned into the riding time components and waiting time components. The riding time components are predicted using the corresponding LSTM models of the clusters while the waiting time components are estimated based on historical bus arrival time records. We evaluated our method on large scale real-world bus travel data involving 30 bus services, and the results show that the proposed method notably outperforms the state-of-the-art approaches for all the scenarios considered.

Benefits of travel time savings by truck platooning in Korean freeway networks

Truck platooning is a promising strategy to not only reduce the workload of truck drivers but also to increase the efficiency of freight transportation. In addition, truck platooning is expected to contribute to the increase in highway capacity, which causes changes in travel demand and travel time patterns. The reliable identification of the impacts of truck platooning based on a scientific and systematic approach is a fundamental task for implementing truck platooning in practice and maximizing its effectiveness. The purpose of this study is to quantify the benefits of travel time savings that would be achieved as a result of truck platooning. A nice feature of the proposed methodology is the integration of microscopic and macroscopic phenomena in its evaluation framework. First, micro-level analysis was conducted to identify the change in highway capacity in a mixed traffic stream consisting of truck platoons and general vehicles. A widely used microscopic traffic simulator, VISSIM, was adopted to quantify the rate of change of the capacity. For the macro-level analysis, the transportation planning software TransCAD was used to estimate the extent of the reduction in travel times based on the increase in the capacity obtained from the micro-level analysis. Simulations based on the proposed methodology were performed on Korean freeways of 3 or more lanes. It was observed that truck platooning would result in annual benefits of travel time savings corresponding to approximately 187.6 billion Korean won (167.7 million US $) in 2020. The results are expected to support various policy-making activities to facilitate the implementation of truck platooning on freeways.

MAC

Urban anomalies have a large impact on passengers' travel behavior and city infrastructures, which can cause uncertainty on travel time estimation. Understanding the impact of urban anomalies on travel time is of great value for various applications such as urban planning, human mobility studies and navigation systems. Most existing studies on travel time have been focused on the total riding time between two locations on an individual transportation modality. However, passengers often take different modes of transportation, e.g., taxis, subways, buses or private vehicles, and a significant portion of the travel time is spent in the uncertain waiting. In this paper, we study the fine-grained travel time patterns in multiple transportation systems under the impact of urban anomalies. Specifically, (i) we investigate implicit components, including waiting and riding time, in multiple transportation systems; (ii) we measure the impact of real-world anomalies on travel time components; (iii) we design a learning-based model for travel time component prediction with anomalies. Different from existing studies, we implement and evaluate our measurement framework on multiple data sources including four city-scale transportation systems, which are (i) a 14-thousand taxicab network, (ii) a 13-thousand bus network, (iii) a 10-thousand private vehicle network, and (iv) an automatic fare collection system for a public transit network (i.e., subway and bus) with 5 million smart cards.

Exploring Preferences for Transportation Modes in an Urban Air Mobility Environment: Munich Case Study

Urban Air Mobility (UAM) is a recent mobility concept with the potential to reduce travel time and change travel patterns. When evaluating the introduction of UAM, understanding the potential users’ choice behavior regarding current available urban transportation modes and autonomous transportation services is essential to demand estimation. This preliminary research intends to gain insight into the travel behavior impacts of autonomous transportation modes, especially UAM, by deriving measures for transportation service attributes and identifying characteristics of potential users who might adopt autonomous transportation services, particularly the services of UAM. Thus, a stated preference questionnaire was designed and distributed in Munich metropolitan region. A main mode choice multinomial logit model and several sub-models, based on market segmentation, were estimated regarding four transportation alternatives: private car, public transportation, autonomous taxi, and autonomous flying taxi. The results indicate that travel time, travel cost, and safety may be critical determinants in autonomous transportation mode adoption. The potential consumers may be willing to pay more for using autonomous transportation modes, especially the service of UAM. Among different market segments, younger individuals, as well as older individuals with high household income, are more likely to adopt UAM. In addition, during the market entry stage, potential travelers may favor UAM particularly for performing non-commuting trips.

Gender, mobility and parental shares of daily travel with and for children: a cross-national time use comparison

Daily mobility varies by gender and is likely related to contextual factors including the gender division of employment and family work, options for modes of transport, and support for work-family reconciliation. This paper compares travel time patterns of men and women using nationally representative time-diary data from Australia, the UK, Spain and Finland (n = 14,176). Despite similarities in men and women's total travel time within countries, results show substantial gender variation in the purpose of daily travel, the transport mode used, who is present, and the way parents in couple-headed households share travel with and for children in relative terms. The extent of the gender gaps vary cross-nationally in ways consistent with prevalent patterns in the gendered division of labour and social parenting norms, but relative gaps in child-serving travel were universal, attesting to the ubiquity of gendered mobility constraints in households with children.

Free-floating car-sharing electrification and mode displacement: Travel time and usage patterns from 12 cities in Europe and the United States

Free-floating car-sharing (FFCS) allows users to book a vehicle through their phone, use it and return it anywhere within a designated area in the city. FFCS has the potential to contribute to a transition to low-carbon mobility if the vehicles are electric, and if the usage does not displace active travel or public transport use. The aim of this paper is to study what travel time and usage patterns of the vehicles among the early adopters of the service reveal about these two issues.We base our analysis on a dataset containing rentals from 2014 to 2017, for 12 cities in Europe and the United States. For seven of these cities, we have collected travel times for equivalent trips with walking, biking, public transport and private car.FFCS services are mainly used for shorter trips with a median rental time of 27 min and actual driving time closer to 15 min. When comparing FFCS with other transport modes, we find that rental times are generally shorter than the equivalent walking time but longer than cycling. For public transport, the picture is mixed: for some trips there is no major time gain from taking FFCS, for others it could be up to 30 min.For electric FFCS vehicles rental time is shorter and the number of rentals per car and day are slightly fewer compared to conventional vehicles. Still, evidence from cities with an only electric fleet show that these services can be electrified and reach high levels of utilization.

Traffic demand estimation for lane groups at signal‐controlled intersections using travel times from video‐imaging detectors

The purpose of this study is to present a new method for lane-based traffic demand estimation using travel times from video-imaging detectors. The method overcomes the following two shortcomings of loop-detector-based algorithms: the fact that the actual demand is unknown when detectors are located upstream from the stop lines within a short distance; and the difficulty in calculating the ratio between streams in different lane groups if detectors are located at the upper reaches of the links. First, the authors analyse a variety of travel time patterns and introduce the concept of a virtual cycle that satisfies the criteria that all vehicles entering into a link in one virtual cycle have just departed from a downstream stop line within a single signal cycle. Next, the authors improve the travel time reduction rate model for queued vehicles in each cycle, and enhance the algorithms to estimate the lane-based traffic demand under different conditions. Finally, all parameters are calibrated and the new models are evaluated. The results show that: the maximum, minimum and average deviations over 12 cycles are 38.50, 0.02 and 16.19%, respectively. The findings in this study have potential applicability for use in traffic control systems, especially where oversaturated conditions are present.

Pattern-Based Time-Discretized Method for Bus Travel Time Prediction

Predicting and providing information about bus arrival time to passengers accurately is a very important aspect of advanced public transportation systems (APTS), a major functional area of intelligent transportation systems. However, the information provided to passengers should be reliable. The reliability of such information provided to passengers depends on the prediction method used, which in turns depends on the input data used in the method. This means that identifying the most significant/appropriate input data and using them in the method are important. So, in the present study, travel time pattern analysis was carried out to find the most significant inputs by performing statistical tests for each day of the week separately. Also, a model-based Kalman filtering algorithm was developed to predict bus travel time by using the identified patterns effectively based on temporal discretization under heterogeneous traffic conditions. The performance of the proposed algorithm showed a clear improvement in prediction accuracy when compared with a prediction method using space discretization.

Bus travel time variability: some experimental evidences

Abstract Bus travel time analysis is essential for transit operation planning. Then, this topic obtained large attention in transport engineering literature and several methods have been proposed for investigating its variability. Nowadays, the availability of large data quantities through automated monitoring allows more in-depth this phenomenon to be pointed out with new experimental evidence. The paper presents the results of some analyses carried out using automatic vehicle location (AVL) data of bus lines and automated vehicle counter (AVC) data on some corridors in the urban area of Rome where the bus services are mixed with other traffic and travel times are subject to high degrees of variability. The results show the effect of temporal dimension and similarity between travel time and traffic temporal patterns, and could open the road for the improvement of the short-term forecasting methods, too.

On Geocasting over Urban Bus-Based Networks by Mining Trajectories

Bus networks in cities have distinctive features such as wide coverage and fixed bus routes so that they show the potential of forming the communication backbone in vehicular ad hoc networks (VANETs). This paper focuses on the geocast in bus-based VANETs and presents a geocast routing mechanism named Vela. Specifically, Vela analyzes and mines historical bus trajectories and characterizes spatial–temporal patterns (i.e., bus travel-time patterns and bus spatial encounter patterns) in a moderate granularity of road segments, which makes the mined patterns both accurate and steady. Furthermore, Vela exploits these acquired patterns to build a probabilistic spatial–temporal graph model and provides the available routing paths with the best possible quality-of-service levels for data delivery requests. Moreover, Vela also employs a two-hop aware strategy that utilizes the real-time spatial–temporal relationships between buses to increase the chances of forwarding the data. The results of the experiments on the real and synthetic trajectories show that Vela performs much better in terms of delivery ratio and delay and has stronger scalability than the other solutions.