Mobile Time Tracker in Transportation Service: A Survey

Bus delay in transportation service is a common issue to be addressed. This issue is verified in a preliminary study conducted earlier among the bus riders. With the proliferation of mobile technology particularly in mobile application development, transportation service providertoday is able to address delay issue usingmobile application. In this study, a GPS based mobile application (App) is proposedto estimatetime arrival (ETA) of buses and an user acceptance test is used to verify the usability of the App. 76 bus riders have completed bus App testing and follow by a survey. The overall results show mobile time tracker is usable and able to solve issue of bus delay and reduce long waiting time. In future work, mobile time trackers associate with other ETA prediction models will be explored and issues such as ETA accuracy will also be addressed.

Modelling the Modal Shift Effects of Converting a General Traffic Lane into a Dedicated Bus Lane

This paper presents an analytical framework for evaluating the performance of dedicated bus lanes. It assumes that under a designated travel demand, the traffic volume on a corridor changes with the modal shifts. The modal shift affects the operations of both bus traffic and car traffic and eventually, an equilibrium bus share ratio that maximizes the performance of the corridor will be reached. Microsimulation modelling is employed to assess the traffic operations under various demand levels and bus share ratios. The results show that converting a general lane into a bus lane significantly reduces bus delay. For car traffic, the overall trend is that delay increases after converting a general lane to a bus lane. In addition, delay decreases with the increase of bus share ratio. Nevertheless, when bus share ratio reaches 0.6 (demand less than 10,000 passengers per hour, pph; or 0.8 when demand increases up to 14,000 pph), there is no significant difference in delay between the two scenarios. The identified bus share ratios have the potential to direct the development of bus lane warrants. Finally, this research recommends that the Transportation Demand Management (TDM) strategies shall be developed to stimulate the modal shifts towards the identified optimal bus share ratio.

Augmenting Transfer Learning with Semantic Reasoning

Transfer learning aims at building robust prediction models by transferring knowledge gained from one problem to another. In the semantic Web, learning tasks are enhanced with semantic representations. We exploit their semantics to augment transfer learning by dealing with when to transfer with semantic measurements and what to transfer with semantic embeddings. We further present a general framework that integrates the above measurements and embeddings with existing transfer learning algorithms for higher performance. It has demonstrated to be robust in two real-world applications: bus delay forecasting and air quality forecasting.

Evaluation of Bus-Bicycle and Bus/Right-Turn Traffic Delays and Conflicts

This research evaluates conflicts and delays caused by interactions among buses, bicycles, and right-turning vehicles at a mixed traffic corridor in Portland, OR. The study site has a near-side bus stop and a right curbside lane designated for buses and right-turning vehicles. Next to the bus/right-turn lane is a bicycle lane with a bicycle box ahead of the bus stop (i.e., between the intersection and the bus stop). This research examines two concerns caused by these overlapping bus, bicycle, and automobile facilities; the first is the number of bus-bicycle conflicts (as a proxy for safety) and the second is bus delay. Video data was collected and analyzed to quantify conflicts, travel time, and delay. For every bus passing through the study site, the mixed traffic scenario that the bus incurs was categorized as one of 72 different combinations of bus, bicycle, and automobile interactions. Video count data was weighted according to seasonal, weekly, and hourly bicycle volume data to estimate the number of annual bus–bicycle conflicts. A regression analysis was performed to identify potential sources of delays. The results indicate that each bicycle crossing the intersection after the bus (within 60 ft of bus) contributes to bus delay. No statistically significant delay was found from the bicycles stopped in the bicycle box, bicycles stopped behind the bicycle box, bicycles that cross the intersection before the bus, or the presence of right-turning vehicles.

Assigning Bus Delay and Predicting Travel Times using Automated Vehicle Location Data

The Washington Metropolitan Area Transit Authority (WMATA) operates 1,250 buses on 168 different routes between 10,600 bus stops to support around 370,000 passengers each day. Utilizing sensors on vehicles and analyzing their location and movements throughout an hour, trip, or day can provide valuable information to a transit authority as well as to the users of a transit system. This amount of information can be overwhelming, but utilizing big data techniques can empower the data and the transit agency. First, this paper develops a methodology for assessing previous delays in the system by applying big data structure and statistical analysis to the data constantly collected by WMATA buses. This method of analysis also helps quantify the impact of potential transit system improvements. Second, the paper describes a model that uses the real-time data, that represents potential delays, to provide future passengers with more accurate arrival predictions despite delays. These analyses are powerful tools for agencies and planners to assess and improve transit service performance using big data analytics and real-time predictions.

Optimizing Transit Signal Priority Implementation along an Arterial

Transit signal priority (TSP) is a common method of providing priority to buses at signalized intersections. The implementation of TSP can affect travel time of cars traveling in the same, opposite, and cross directions. The bus delay savings and car travel-time impacts are not expected to increase linearly when considering multiple intersections along an arterial. This paper quantifies the influence of TSP on arterials with dedicated bus lanes considering an arterial-wide approach utilizing variational theory. Existing tools were modified to quantify the change in capacity along an arterial where TSP was implemented and it was shown that this effect was negligible. In addition, the bus delay savings and cross-street capacity losses were determined. Case studies provided insights into the influence of TSP among different network homogeneities and bus frequencies. Using these tools, an optimization framework was developed to determine where to implement TSP along an arterial to maximize the marginal benefits, or minimize marginal costs. In addition, a comparison of evaluating an arterial as a sum of isolated intersections as opposed to evaluating an arterial as a whole is presented. This analysis indicates the necessity of the arterial-based method in considering TSP impacts along corridors.

OPTIMIZING PHASE COMPRESSION FOR TRANSIT SIGNAL PRIORITY AT ISOLATED INTERSECTIONS

Transit signal priority (TSP) is a promising low-cost strategy that gives preferential treatments for the buses to go through intersections with minimum delay time. In this paper, a new TSP control model was presented for isolated intersections to minimize bus delay and to reduce the impact of TSP on other vehicles by optimizing signal control phase selection and compression. This paper starts with the phase selection and compression strategies to provide treatments to bus priority requests. Then, two new features on phase selection and compression aspects are applied to TSP, i.e. the time that a bus priority request needs is provided by the phase(s) with the lowest traffic volume, and multi-phases can be selected to serve a bus request. Field data are collected from a major traffic corridor in Changzhou (China) and applied for VISSIM simulation. The proposed TSP control model as well as the fixed-time control and the conventional TSP control models are tested and compared under different traffic demands, headways and maximum saturation degrees. The comparative results showed that the proposed model outperformed the conventional TSP control model in terms of reducing bus delay, minimizing the impact on other vehicles and reducing the stop rate for buses. This paper reveals that, the proposed TSP strategy can significantly optimize the phase compression process and improve transit efficiency.

Does Combining Transit Signal Priority with Dedicated Bus Lanes or Queue Jump Lanes at Multiple Intersections Create Multiplier Effects?

An extensive body of literature deals with the design and operation of public transport (PT) priority measures. However, there is a need to understand whether providing transit signal priority with dedicated bus lanes (TSPwDBL) or transit signal priority with queue jump lanes (TSPwQJL) at multiple intersections creates a multiplier effect on PT benefits. If the benefit from providing priority together at multiple intersections is greater than the sum of benefits from providing priority separately at each of those individual intersections, a multiplier effect exists. This paper explores the effects of providing TSPwDBL or TSPwQJL at multiple intersections on bus delay savings and person delay savings. Simulation results reveal that providing TSPwDBL or TSPwQJL at multiple intersections may create a multiplier effect on one-directional bus delay savings, particularly when signal offsets provide bus progression for that direction. The multiplier effect may result in a 5% to 8% increase in bus delay savings for each additional intersection with TSPwDBL or TSPwQJL. A possible explanation is that TSPwDBL and TSPwQJL can reduce the variations in bus travel times and thus allow signal offsets—which account for bus progression—to perform even better. Furthermore, results show little evidence of the existence of a multiplier effect on person delay savings, particularly for TSPwQJL with offsets that favor person delay savings. A policy implication of these findings is that considerable PT benefits can be achieved by providing both time and space priority in combination on a corridorwide scale.