Travel Time Components Research Articles

A Study of Delhi Metro’s Magenta Line to Assess Commuter Satisfaction Post Covid-19

Abstract This study looks how satisfied people are with the Delhi metro, which is crucial for sustainable transportation. The study focuses on security, comfort, travel time components. The research shows how important the quality of service is in public transportation and how it affects what people think and if they would recommend it to others. In the research, 430 passengers surveyed and used statistical analysis to understand what factors influence people’s opinions. The study reveals that age, gender, comfort and security are important factors. The study also investigates the alternate PT modes and found that metro fare, seating/standing facilities are important aspects especially for different genders. It’s important for the DM to think about how people feel about the cost of using it, the seating, and the different preferences of men and women. Making changes like lowering fares and making the metro more comfortable could make it a better option for everyone in Delhi.

VARIABILITY OF PARATRANSIT TRAVEL TIMES: THE CASE OF KUMASI, GHANA

ABSTRACT While much has been reported about bus travel times and their variability for formal bus services, little is known about travel time variation for paratransit, the dominant means of transportation in most low- and middle-income countries (LMICs). This study quantifies the components of paratransit travel time on a selected route in Kumasi, Ghana. It analyzes the variability of travel times within the day and from day to day. A mobile phone app was employed to conduct a travel time survey onboard paratransit vehicles on the study route. GPS and stop-related data were collected. Various travel time variability measures and heat map was used for within day and day-to-day variability analysis in both directions of the study section. About 16% of travel time in the study section was spent dwelling (boarding and alighting). The variation in travel times across the day was comparatively higher than those of formal bus services and fluctuated across the day with no distinct pattern within any given time period. Both early and late trips contributed to this variation across the day. Fridays had significantly different variability from other weekdays.

ANALISIS BOBOT KOMPONEN WAKTU PADA PERJALANAN MENGGUNAKAN ANGKUTAN UMUM PERKOTAAN

Traffic congestion is a serious problem in many cities in the world. Congestion will be reduced if most private vehicle users want to switch to using Urban Public Transport (AUP) when traveling. In order for AUP to be in demand, AUP services must be better. One of the most important services in attracting AUP passengers is the short travel time. The intended travel time is a combination of time to the stop, waiting time at the stop, time in the vehicle, transfer time, and travel time from the stop to the final destination. In order for travel time to be calculated, the weight of each component of travel time must be known. The purpose of this study was to determine the weight of each type of travel time component by taking the case of the Trans Jogja Bus (BTJ). The analysis was carried out using the Analytical Hierarchy Process (AHP) method. Data were obtained by conducting interviews / distributing questionnaires both in person and online, to 103 young respondents in urban Yogyakarta. Based on the results of the study, it was found that the components of travel time with weights from the largest in succession were waiting time at the bus stop, followed by time to the bus stop, transfer time, time from the bus stop to the final destination, and time in the vehicle. These results indicate that reducing waiting time at bus stops is a priority program that needs to be done to shorten the total travel time so that AUP becomes attractive.

Proportional Estimation Method of Urban Rail Passenger Flow Transfer Path Selection Based on IC Card Data

Based on the analysis of the influencing factors of urban rail transit passenger flow transfer path selection behavior and the components of travel time, a method of estimating the proportion of urban rail transit passenger flow transfer path selection based on IC card data is proposed. Firstly, the research determines the shortest interchange path algorithm as Dijkstra algorithm and the graph-based depth-first search algorithm as the effective interchange path search algorithm; analyzes the factors influencing the passenger flow interchange path selection behavior as three types of travel time, interchange cost and road network familiarity; constructs the generalized cost function of effective interchange path selection by combining travel time and interchange cost; establishes the passenger flow probability selection model by combining road network familiarity. To eliminate the influence of absolute difference of utility on the probability of path selection in the polynomial logit model, the path probability selection model is improved with the help of minimum travel cost; Bayesian estimation is used to compare the passenger travel time with the theoretical travel time to identify the closest effective interchange path, and the joint distribution probability is used to calibrate the probability selection model in terms of travel time and interchange cost. The coefficient, coefficient of interchange passenger flow, penalty coefficient of interchange number, familiarity of road network and other related parameters are combined with the joint distribution probability to calibrate the probability of selection of different effective interchange paths.

Application of modular vehicle technology to mitigate bus bunching

The stochastic nature of public transport systems leads to headway variability and bus bunching, causing both operator and passenger cost to increase significantly. Traditional strategies to counter bus bunching, including bus-holding, stop-skipping, and bus substitution/insertion, suffer from trade-offs and shortcomings. Autonomous modular vehicle (AMV) technology provides an additional level of flexibility in bus dispatching and operations, which can offer significant benefits in mitigating bus bunching compared to strategies available with conventional buses. This paper introduces a novel alternative to stop-skipping by leveraging the new capabilities offered by AMVs (in particular, en-route coupling and decoupling of modular units). We develop a simple bus-splitting strategy that directs a modular bus to decouple into individual units when it experiences a headway longer than a given threshold. We then use a macroscopic simulation to present a proof-of-concept evaluation of the proposed modular strategy compared to a benchmark traditional stop-skipping strategy and the base (no control) case. We find that the proposed strategy outperforms the benchmark in decreasing each of the three travel time components: waiting time, in-vehicle time, and walking time (which it eliminates completely). It therefore reduces the overhead of bus bunching and thus the travel cost by more than twice as much as the benchmark for busy bus lines. Simultaneously, it also reduces headway variability to a comparable degree. Furthermore, we analyze different control thresholds for applying the proposed strategy, and show that it is most effective when applied proactively, i.e. with the control action being triggered even by small headway deviations.

Measures of accessibility incorporating time reliability for an urban rail transit network: A case study in Wuhan, China

Time Reliability (TR) affects the accessibility of an Urban Rail Transit Network (URTN), which refers to the reachability of destination stations in this paper. However, existing studies do not incorporate TR into accessibility metrics when measuring a URTN’s accessibility. Here, the Buffer Time Index (BTI), which is the ratio of extra “buffer” time needed for arriving reliably to the average Travel Time (TT), is used to measure TR. Time-based, cost-based, and timebound-destination-reachability metrics which incorporate TR are proposed to measure the accessibility of a URTN. A Monte Carlo simulation is applied to estimate TR according to the probability distributions of TT components, which overcomes the difficulty of obtaining many passengers’ data. The accessibility of Wuhan’s URTN during weekday morning peak hours indicates that the metrics which do not consider TR overestimate a URTN’s accessibility. The evaluation results can effectively guide decisions on improvements to a URTN’s accessibility. The advantage of the measures of accessibility incorporating TR for a URTN is that they not only improve the reasonableness of evaluation but also reflect an aspect of great concern to passengers.

A New Performance Index for Evaluating Transit Quality of Service

This article proposes a new performance index called Transit Service Indicator (TSI), which could be used as a comprehensive measure for quantifying the quality of service of a transit system. TSI integrates multiple performance measures (e.g., service frequency, hours of service, route coverage, and travel time components) within a systematic framework. It takes into account spatial and temporal variations in travel demand, recognizing that quality of service is a result of interaction between supply and demand. A case study is conducted to examine the sensitivity of the proposed TSI to various system design and condition variables and parameters.

Post-COVID-19 performance evaluation of urban metro transit system in Delhi and influence on access mode

The impact of the COVID-19 pandemic has been observed to affect the travel patterns, routes and traffic in public transportation systems across the world. It is important to evaluate the performance of the Delhi Metro (DM) post-COVID-19 pandemic for its successful operation. In this study, the BLUE line of DM with the longest route and highest number of metro stations has been examined for performance evaluation. The performance is evaluated based on travel time components (access, egress, transfer, waiting and main haul time) to calculate various performance indicators i.e., Level of Service (LOS), Service Time Ratio (STR), Passengers Waiting Index (PWI), Total Travel Ratio (TTR) and Interconnectivity Ratio (IR). The post-COVID-19 LOS evaluation indicates that the users are spending 72.6 % to 84.4 % of their main haul time on their access-egress trips. The STR shows that the users are spending 10.9 % to 12.6 % of their total travel time in waiting and transferring only during the main haul trip. The mean PWI, RI and TTR are noted as 1.008, 0.794 and 2.069 respectively. The IR is observed as 0.312 for the given route. The median and average main haul distances across all access modes are observed to be (12–21) Km. and (19.69 ± 11.19) Km. respectively. It is revealed that the observed mean value of LOS is (0.775 ± 0.575). It is further revealed that the metro fare per trip and the access-egress trip cost per day are significant factors for access mode choice in the case of walking and auto-rickshaw whereas LOS, RI and PWI are other significant operator’s performance indicators influencing the access mode choice. The study reveals that post-COVID-19 the performance indicators exhibit the unsatisfactory performance of DM and there is further scope to improve the UMTS performance.

Does the Inclusion of Spatio-Temporal Features Improve Bus Travel Time Predictions? A Deep Learning-Based Modelling Approach

With the abundance of public transportation in highly urbanized areas, it is common for passengers to make inefficient or flawed transport decisions due to a lack of information. The exact arrival time of a bus is an example of such information that can aid passengers in making better decisions. The purpose of this study is to provide a method for predicting path-based bus travel time, thereby assisting accurate bus arrival and departure time predictions at each bus stop. Specifically, we develop a Geo-conv Long Short-term Memory (LSTM) model that (1) extracts subsequent spatial features through a 1D Convolution Neural Network (CNN) for the entire bus travel sequence and (2) captures the temporal dependencies between subsequences through the LSTM network. Additionally, this study utilizes additional variables that affect two components of bus travel time (dwelling time and transit time) to precisely predict travel time. The constructed model is then evaluated by the practical application to two bus lines operating in Seoul, Korea. The results show that our model outperforms three other baseline models. Two bus lines with different types of operation show different model performance patterns that are dependent on travel distance. Interestingly, we find that the variable related to the link of the stop location appears to play an important role in predicting bus travel time. We believe that these novel findings will contribute to the literature on transportation and, in particular, on deep learning-based travel time prediction.

Modeling of BRT System Travel Time Prediction Using AVL Data and ANN Approach

Improving the quality of public transportation systems and encouraging passengers to use them are effective solutions for reducing transportation problems in metropolitan. Prediction of travel time and providing information to passengers are significant factors in this process. In this research not only the travel time components in Bus Rapid Transit (BRT) system were investigated but also an Artificial Neural Network (ANN) model and a regression model for travel time prediction were presented. To enhance this aim, data was collected by AVL data and field observation and after investigating the primary independent variables, the significant ones were determined using statistical analysis, then ANN development was done. Moreover, linear regression method was used for this purpose. The results prove that although both models have high level of prediction accuracy, ANN model outperform the regression model and the accuracy for the route sections with no signalized intersections is higher than the others.

Mode boundaries of automated metro and semi-rapid rail in urban transit

Our research question is to what extent, and under what circumstances, full automation in metro lines defines transit mode boundaries with respect to semi-rapid transit. The modeling approach is based on micro-economic appraisal. Automation, beside changing the investment and operation and maintenance cost profiles of metro lines, can improve some aspects of the user experience. The low marginal cost of frequency possible with automated metro may unlock both users’ benefits via reduced waiting times and crowding, and operator’s savings via shorter trains and smaller platforms and stations. We show how the user’s travel time components are structurally different under several configurations. In particular, we highlight the critical role that different demand and alignment patterns play in the mode comparison.

Values of Travel Time for Recreational Trips under Different Behavioural Rules

In this study, discrete choice models that combine different behavioural rules are estimated to study the visitors’ preferences in relation to their travel mode choices to access a national park. Using a revealed preference survey conducted on visitors of Teide National Park (Tenerife, Spain), we present a hybrid model specification—with random parameters—in which we assume that some attributes are evaluated by the individuals under conventional random utility maximization (RUM) rules, whereas others are evaluated under random regret minimization (RRM) rules. We then compare the results obtained using exclusively a conventional RUM approach to those obtained using both RUM and RRM approaches, derive monetary valuations of the different components of travel time and calculate direct elasticity measures. Our results provide useful instruments to evaluate policies that promote the use of more sustainable modes of transport in natural sites. Such policies should be considered as priorities in many national parks, where negative transport externalities such as traffic congestion, pollution, noise and accidents are causing problems that jeopardize not only the sustainability of the sites, but also the quality of the visit.

To Pool or Not to Pool? Understanding opportunities, challenges, and equity considerations to expanding the market for pooling

On-demand mobility services such as bikesharing, scooter sharing, and transportation network companies (TNCs, also known as ridesourcing and ridehailing) are changing the way that people travel by providing dynamic, on-demand mobility that can supplement public transit and personal-vehicle use. Adoption of on-demand mobility has soared across the United States and abroad, driven by the flexibility and affordability that these services offer, particularly in urban areas where population density and land use patterns facilitate a reliable balance of supply and demand. The growth of app-based ridesharing, microtransit, and TNCs presents a unique opportunity to reduce congestion, energy use, and emissions through reduced personal vehicle ownership and increased vehicle occupancy, the latter of which is largely dependent on the decisions of individual travelers to pool or not to pool. This research provides key insights into the policy levers that could be employed to reduce vehicle miles traveled and emissions by incentivizing the use of pooled on-demand ride services and public transit. We employ a general population stated preference survey of four California metropolitan regions (Los Angeles, Sacramento, San Diego, and the San Francisco Bay Area) to examine the opportunities and challenges for drastically expanding the market for pooling, taking into account the nuances in emergent travel behavior and demand sensitivity across on-demand mobility options. Although high-frequency TNC users – those that use TNCs once a week or more - are more likely to consider pooling than less frequent users and reflect more multimodal travel behavior than other travelers, we find that the most captive and price sensitive TNC users are often the most vulnerable. Heavy TNC users – those using TNCs more than three days per week - are disproportionately low income, more likely not to own or lease a car and more likely to use TNCs for essential trip purposes than are less frequent users. Pooling demand sensitivity varies significantly across trip contexts, metropolitan regions, socio-demographics, travel behavior, and attitudes and perceptions toward sharing. We estimate the time and price tradeoffs in choosing between ride alone and shared on-demand service options, finding significant differences across values that travelers place on each component of travel time (wait time, access/egress walking time, and in-vehicle time) by geography and income level. We discuss the potential to leverage these insights to develop policies that combine pricing, curb management, and promotional strategies to increase the pooling market share.

Estimation of lane-level travel time distributions under a connected environment

Travel time distribution estimation is fundamentally important for the evaluation of travel time variability and reliability. For urban roads, signal delays are key components of travel time. They are stochastic and differ for vehicular movements due to different signal timings for through, left-turning, and right-turning vehicles. To better assist travelers in making trip decisions under connected environments, this study seeks to investigate lane-level travel time distributions for signalized arterial roads by specifically considering the impacts of both the link travel time and the vehicle movement-based signal delays at an intersection. A simulation testbed based on the VISSIM microscopic traffic simulator and a Java plugin is developed to mimic the traffic flow dynamics of a signalized arterial, El Camino Real, in Palo Alto, California, under a connected environment. The detailed vehicle trajectory data obtained from the simulation can be leveraged to obtain lane-level travel time information. Typical normal, lognormal, gamma, and Weibull distributions, as well as kernel density estimation (KDE), are adopted to calibrate the lane-level travel time distributions. The estimation results demonstrate that conventional distribution models are suitable for estimating the travel time distributions of only a few road segments, while KDE fully captures travel time reliability metrics such as the buffer time index, skewness, and width of the travel time distributions for all road segments. This result will help traffic managers and engineers carry out effective traffic management and control to optimize the operation of arterial roads.

Electric scooter sharing: How do people value it as a last-mile transportation mode?

With its advantages in urbanized areas, the electric scooter sharing (ESS) system has received considerable popularity as a transportation mode for short-distance trips. In this paper, people’s preferences toward ESS for last-mile trips were analyzed using a stated preference experiment. We designed the experiment to scrutinize how people value ESS-riding compared with other conventional last-mile transportation modes. The values of travel time components such as access and riding times are estimated using a mixed logit model. The values of riding transportation modes were significantly greater than that of walking for a last-mile trip. However, none of the time values of two modes—those from a conventional last-mile mode and ESS—was dominating because the difference in the dispersions of each made the time value curves intersect around their medians. Considering the results, we have concluded that ESS is a competitive transportation mode in last-mile situations.

Possibilistic Approach for Travel Time Reliability Evaluation

Travel time estimation & reliability evaluation of any means of transportation in every type of travel mode- land, rail, sea and air has been of immense interest of the researchers; primarily due to growing economic concern in the field of logistics & passenger movement. In situations like quantitative data inaccessibility or data imprecision, fuzzy set based possibilistic approach is recognized as a practical choice in obtaining the reliability estimates. This paper proposes and advocates possibilistic approach for travel time reliability computation of any type transportation vehicle under fuzzy type of data. The proposed approach is a novel way of computing the travel time & obtaining the related reliability value. Initially, the paper proposes the general methodology for travel time reliability evaluation. Individual travel time components of a transportation vehicle are considered as fuzzy; as a result, travel time is modelled as a fuzzy variable. Travel time reliability of a transportation vehicle has been defined with the help of possibilistic measures. The proposed procedure is then demonstrated with an application to marine vessel carrying the bulk. After illustration of the proposed methodology, sensitivity analysis is carried out. The paper ends with the comments on comparative features of the three cases.

GPR-based depth measurement of buried objects based on constrained least-square (CLS) fitting method of reflections

The accurate measurement of depth of buried objects using GPR with a common-offset antenna (fixed distance between transmitter and receiver) requires two measurement parameters: (1) the GPR wave propagation velocity in the host material surrounding the buried objects; and (2) the two-way travel time when the GPR antenna is directly above the buried objects. Sham and Lai (2016) proposed a refined GPR wave travel path model in concrete when a cylindrical object is encountered. The model considers the factors of the object’s (or in this paper the underground pipe’s) radius and separation distance between the antenna transmitter and receiver. Based on this model, a constrained least-square (CLS) fitting method was applied in order to simultaneously address the trigonometrical, velocity estimation and two-way travel time components, thereby allowing the depth of the underground pipe to be calculated. One laboratory experiment in air and two sets of validation experiments in various soil environments were conducted. The results are also compared with those produced by point-based velocity measurement method (ASTM algorithm) (ASTM, 2011) and by normal least-square fitting method (Levenberg-Marquardt algorithm). The research has shown that: (1) the proposed algorithm is low-cost in terms of calculation because no iteration and no convergency analysis is required; (2) this algorithm is applicable to all the cases discussed here and is robust for GPR data with a certain degree of noise; (3) this method helps to significantly reduce the percentage error of estimated depth from 10.0% (ASTM method) and 3.7% (L-M method) to 2.4% (CLS method) on average, while the discrepancy between estimated depth and actual depth can be reduced to the order of several centimeters. Based on an accurately estimated velocity, further work on host material characterization and water content measurement can then be conducted.

Effects of proximity to markets on dairy farming intensity and market participation in Kenya and Ethiopia

The effect of proximity to markets on dairy farming intensity and market participation traditionally has been viewed as a market quality effect stemming from distance to end-markets with resultant travel time. This study departs from this by distinguishing three travel time components: travel time to local service center for inputs and services, to dairy delivery point, and to end-markets. Dairy farms in nine villages each in Ethiopia and Kenya were sampled and interviewed along a double proximity gradient. Effects on many production and marketing parameters were measured and compared using regression analysis, to test the hypothesis that intensity of dairy farming and degree of market participation increase with proximity to end-markets and with proximity to local service centers. Findings prove the hypothesis for proximity to local service center, which causes better market quality for inputs and outputs, smaller farms with less available labor, use of more purchased feeds and services, higher stocking rates, higher yields, and higher margins per hectare. Findings only partly prove the hypothesis for proximity to end-markets, mainly due to unexpected land scarcity in the most remote locations. Low productivity and low dairy farming intensity and market participation for remote farms in Ethiopia are attributed to limited and volatile market demand, a coarse milk-collection grid, and low quality of input and service markets, which are largely publicly organized. Implication of this study is that the common typology of dairy farms in ‘(peri-) urban’ and ‘rural’ farms needs adjustment by outlining local market access and connectivity. ‘Remote’ rural farms need to be connected to milk collection infrastructure, input shops and services to even have the choice to increase participation in dairy- or other markets.

Review on Statistical Modeling of Travel Time Variability for Road-Based Public Transport

Accurately modeling travel time of road-based public transport can help directly improve current passenger service and operating efficiency. Moreover, it paves the way for control of future high technology automated vehicles, which will share the same characteristics of sharing the road infrastructure with other vehicles; carrying multiple passengers, i.e. having a non-negligible dwell process, and run not completely demand-responsive but in general following a schedule or a target frequency. Recent advances in sensor and communications technology, leading eventually to comprehensive vehicle connectivity, have significantly increased the amount and quality of travel time data available making it possible to better model distributions of travel time of current buses. We assume that the choice of those distributions with regards to transport performance will hold also in the near future. This paper explains definitions of travel time components and explains how they contribute to variability. It focuses on the description of day-to-day variability and systematically reviews the current state of the art for statistically modeling bus travel, running, and dwell time distributions. It considers statistical distributions developed based on empirical data from the research literature. Statistical distributions are powerful tools, as they can describe the inherent variability in data with a limited number of parameters. The review finds that both spatial and temporal data aggregation have an important influence on the statistics as well as the choice of the most appropriate probability distribution. This influence is still not well understood and remains a question for further studies. Furthermore, the review finds that mixture distributions provide good fitting performance, however, it is important to improve the description of components in such distributions, in order to get meaningful and understandable distributions. The methodologies for fitting distributions, for proving if a distribution is suited, and for identifying best fitting, robust and reproducible distribution should be reconsidered. Such a distribution will enable reporting, controlling operations, and disseminating information to operators and travelers. Finally, this review proposes directions for further work.

Transit Holding Control Model for Real-Time Connection Protection

The time required to transfer between different transit lines is a critical component of passenger travel time. Although transit agencies attempt to design well-coordinated timetables among intersecting lines at the scheduling stage, an operational control method is necessary to maintain the coordinated transfers that may occasionally be disrupted due to unexpected delays of public transit (PT) vehicles. One possible and practical approach is Connection Protection (CP), which involves holding a transit unit in order to wait for another transit unit that is planned to provide a coordinated transfer but has been delayed. This study develops a CP model to apply holding control to a receiving vehicle trip for the purpose of protecting the scheduled connection against delay of a feeder trip. The study incorporates the probabilistic nature of transit operations in formulating the cost function of the model, and accordingly makes more robust decisions for controlling the PT system. The developed model is evaluated by means of a sensitivity analysis. The results show that the model improves transfer efficiency and reduces the waiting times of affected passengers while minimizing delays to other passengers.