Traffic Assignment Process Research Articles

Integrated Generalized Cost Model Considering the Whole Trip for Urban Rail Transit Station Choices

Convenient transfer between urban rail transit (URT) and feeder modes could be fundamental for improving the URT service. In the field of URT, most studies have attempted to estimate the station choice of sub-trips like access trips and egress trips rather than the whole trip, which may be locally optimal and inconsistent with the traveler’s behavior of maximizing the utility of the whole trip. In this study, we build an integrated generalized cost model for URT to estimate the access and egress station choices of URT travelers from the perspective of the whole trip and apply a genetic algorithm to estimate the model parameters. Data used in this study include a revealed preference (RP) mode chain survey conducted in Nanjing, China, and the travel cost data obtained from an online navigation application program interface (API), thus reducing the transit networking modeling work as the conventional traffic assignment process. The proposed model has promising station choice prediction accuracies for whole trips. The results show that: (a) travelers do not always choose the nearest station, and the whole trip’s generalized cost would affect their choices; (b) travelers make heterogeneous station choices. They have a varied perception of time and fare and are more sensitive to access trips; and (c) in forecasting-oriented applications, the model parameters can be set flexibly, and the model can maintain acceptable forecasting accuracy. The findings contribute to understanding station choice mechanism, transfer optimization, and feeder demand forecast for stations.

Disrupted transportation networks under different information availability and stochasticity situations

Disruptive events may result in severe performance degradations in transportation networks. Properly evaluating the performance deterioration of a disrupted network with taking individual’s behavioral response into account is a critical step within many operational and planning decisions, such as emergency rescue, traffic management, evacuation planning, infrastructure reinforcement, and so on. The focus of this paper is on the development and analysis of a set of disrupted network evaluation methods, considering different disruption information availability conditions and further an adaptation of these methods into a stochastic traffic environment and stochastic disruption context. Specifically, we define two typical information availability conditions: 1) information globally available (IGA); 2) information locally available (ILA), in terms of different data collection and transmission capabilities and traveler response behaviors. The IGA condition presumes that travelers know the complete set of disruption information before they make trips, while the ILA condition, however, represents another extreme information attainment condition, implying that travelers can observe or notice the occurrence of a disruption only when they reach in person the proximity of the disruption site. A new dynamic adaptive routing process is constructed to model individual routing behaviors for the ILA condition. The above modeling framework implies that, the total travel cost in the IGA condition is always better than the ILA situation in the deterministic traffic environment, and this conclusion is also applicable to the total traveler surplus in the IGA and ILA conditions in the stochastic traffic environment. On the other hand, the resulting ILA network flow pattern in the stochastic traffic environment can be fully accommodated by a Markovian traffic assignment process. When disruptions probabilistically occur, the proposed network evaluation method resorts to a combinatorial number of network evaluations and poses a very time-consuming process. For tackling this computational complexity, we suggested a Monte Carlo simulation-based evaluation framework and found that it can very precisely conduct network evaluations in a much more efficient manner, especially when the number of independently occurring disruptions is large.

Modelling the Impact of Rain in Traffic Assignment Procedures

The efficiency of urban transportation system is un-der the influence of weather conditions. It is necessary to incorporate these impacts into transport system analysis, in order to prepare adequate mitigation measures. Trans-port models are often used in different types of transport system analysis and forecasting of its future characteris-tics. This paper focuses on implementation of the impact of rain in transport modelling, particularly into a traffic assignment process as a part of a macroscopic transport model. This aspect of modelling is important because it can indicate parts of the network where this impact leads to a high volume/capacity ratio, which is a good input for defining mitigation measures. Commonly, transport models do not consider weather impacts in its standard procedures. The paper presents a methodology for cali-brating volume-delay function in order to improve traf-fic assignment modelling in case of rain. The impact of different rain categories on capacity and free-flow speed was quantified and implemented in the volume-delay function. Special attention is given to the calibration of the part of volume-delay function for over-saturated traf-fic conditions. Calibration methodology is applicable for different types of volume-delay functions and presents a solid approach to incorporate weather conditions into common engineering practice.

A dynamic programming optimization for traffic microsimulation modelling of a mass evacuation

• A Framework for using all modes, including transit and school buses in evacuation. • Optimize auto-bus composition for an efficient evacuation. • A case of Halifax is tested with a 5–20% auto-based demand served by buses. • Results from all mode evacuation yield a vehicular traffic reduction of 3.9–7.7%. • An evacuation time reduction of 9–22.7% is achieved in all mode evacuation. This study develops a novel framework to formalize the optimal utilization of all available modes of transportation , particularly transit and school buses for a mass evacuation. The study develops an “All-Mode Evacuation Decision Support Tool (AMEDST)” to determine an optimum auto-bus composition that yields an improvement in evacuation time and network congestion. The study follows a Knapsack optimization and adopts a solution algorithm called Dynamic Programming within a Python platform to optimally allocate buses to evacuees exposed to higher level of vulnerabilities. A traffic microsimulation model follows a dynamic traffic assignment process to simulate evacuation scenarios using all available modes. Results from the traffic simulation yield a vehicular traffic reduction of 3.9–7.7% and an evacuation time reduction of 9–22.7% if 5–20% of auto-based demand are served by buses. The tool will help emergency personnel evaluate alternative scenarios for making informed decisions regarding resource allocation and emergency budget policies for large-scale evacuations.

Efficient execution plan for egress traffic engineering

The main objective of Egress Peer Engineering (EPE) is to steer traffic exiting one Autonomous System (AS) to another in the most cost-effective way by assigning network traffic flows of different destinations to internal routes specific to every AS. The traffic assignment process is carried out to satisfy network operators’ objectives ,which include optimizing resource utilization, minimizing monetary costs and avoiding overloading the peer links. Due to network traffic dynamicity and unpredictability, traffic assignments should be constantly updated so that the aforementioned objectives are satisfied. Each of these updates results in traffic assignment changes that transition the network to a more optimized state. Executing these changes all at once is detrimental to the internal network infrastructure. To tackle this issue, this work targets finding execution plans involving several intermediate steps whereby each step includes a subset of traffic assignment changes. While executing these steps, the network operator’s objectives need to be guaranteed. To that end, an oracle algorithm that generates all the possible balanced subsets of traffic changes as execution plans is formulated. This algorithm is compared to two heuristics that are designed based on an analytical study of the problem itself. To effectively evaluate these algorithms, evaluation criteria are devised that encompass several technical and design quality metrics of the desired execution plan. These three approaches are evaluated on network configurations of small size networks, and the results obtained show that one of the heuristics outperforms the oracle implementation in terms of execution time while producing comparable results based on the evaluation criteria. For big networks, the best performing heuristic satisfies the quality metrics and generates its best execution plan in a short period of time.

EvoTSC: An evolutionary computation-based traffic signal controller for large-scale urban transportation networks

Traffic Signal Control (TSC) is a crucial component in the modern intelligent transportation systems. Typically, the TSC can be formulated as a bilevel optimization problem, which is comprised of the signal timing and the traffic assignment modules. The problem is challenging that the existing approaches usually endure a huge computational cost. As a result, many TSC approaches focus on relatively simple and small transportation networks, which do not satisfy the practical situations. To address above issues, this paper proposes an evolvable TSC (EvoTSC) system, which adopts nature-inspired techniques to realize the global optimization of the TSC in large-scale urban transportation networks. Particularly, it involves two evolutionary computation components. The first component is an Adaptive Differential Evolution (ADE) to optimize the signal timing. Meanwhile, the traffic assignment process is included in the solution evaluation of the ADE to react to the traffic flow dynamics. The second component is an off-line Niching Ant Colony Optimization (NACO), which aims to provide the traffic assignment with sets of multiple promising routes beforehand. This way, the EvoTSC system avoids repeatedly building candidate routes for the traffic assignment, which can greatly save the computational cost of the ADE to evaluate each solution in a large-scale transportation network. In experiments, we carry out comparisons of different TSC approaches on both synthetic and practical transportation networks. The experimental results validate the effectiveness of the proposed EvoTSC system.

A Multi-objective Network Design Model for Post-disaster Transportation Network Management

Despite their inherent vulnerability to structural and functional degradation, transportation networks play a vital role in the aftermath of disasters by ensuring physical access to the affected communities and providing services according to the generated needs. In this setting of operational conditions and service needs which deviate from normal, a restructuring of network functions is deemed to be beneficial for overall network serviceability. In such context, this paper explores the planning of post-disaster operations on a network following a hazardous event on one of the network’s nodes. Lane reversal, demand regulation and path activation are applied to provide an optimally reconfigured network with reallocated demand, so that the network performance is maximized. The problem is formulated as a bi-level optimization model; the upper level determines the optimal network management strategy implementation scheme while the lower level assigns traffic on the network. Three performance indices are used for that purpose: the total network travel time (TNTT), the total network flow (TNF) and the special origin-destination pair (OD pair) accessibility. A genetic algorithm coupled with a traffic assignment process is used as a solution methodology. Application of the model on a real urban network proves the computational efficiency of the algorithm; the model systematically produces robust results of enhanced network performance, indicating its value as an operation planning tool.

Spatial and Temporal Analyses for Estimation of Origin-Destination Demands by Time of Day Over Year

This paper proposes a two-stage model for the estimation of origin-destination (OD) demands by the time of day over the year with the use of offline traffic data from the real-time travel information system. In the first stage, a travel time recursive function is proposed to use the offline travel speed data for the investigation of the spatial and temporal relationships between time-dependent OD demands and traffic counts. As such, it is not required to carry out the time-consuming dynamic traffic assignment (DTA) process which is frequently used in the conventional time-dependent OD estimation models. Using the results in the first stage together with the available traffic count data, a least-squares method is adopted to formulate the time-dependent OD demand estimation problem as a quadratic programming model in the second stage. A solution algorithm is adapted for solving the proposed model. Then, the proposed method is easy for implementation in practice. Particularly, when the traffic accident occurs in the network, the estimated time-dependent OD demands can be helpful for understanding the complex travel behavior (e.g., departure time choice) under uncertainty condition. The numerical examples are presented to illustrate the applications of the proposed model.

Mass Evacuation of Halifax, Canada: A Dynamic Traffic Microsimulation Modeling Approach

This study develops a dynamic traffic microsimulation model for testing a mass evacuation of the Halifax Peninsula, Canada. The model is developed utilizing multiple data sources and calibrated using a Latin Hypercube (LH) sampling technique. The proposed microsimulation modeling framework adopts a Dynamic Traffic Assignment process to capture temporal variations in travel time and traffic congestion propagation during evacuation in the Halifax transport network. The study demonstrates the efficacy of the model in predicting traffic flows for evacuation due to floods of water level 2.9m, 3.9m, and 7.9m. Network level impacts as well as individuals’ evacuation experiences are examined utilizing the developed model. The results suggest that it takes 22 hours to evacuate the Peninsula in the case of a flood of water level 2.9m. In case of all scenarios, it takes 6-8 hours to complete 50% evacuation, whereas the rest is evacuated in 14-16 hours depending on the extent of the road damages caused by the floods. Individuals departing early experience higher network and local level traffic congestion during evacuation. The model results will be useful for policy makers to develop evacuation plans and countermeasures.

TRAFFIC ASSIGNMENT MODEL OF AL-AMARAH CITY

The initiation of this study was made with the objective of loading or assigning the flow of traffic in the base and target year (expressed as a matrix identifying the number of trips generated and distributed in the previous study from each Traffic analysis zone (TAZ) to every other within the study area) on a road network of Al-Amarah City. The traffic assignment process is usually implemented in two steps. The first step is constructing the network dataset of road network of Al-Amarah city for obtaining the optimal route between each node to all other nodes depending on two criteria; distance and time for evaluation the accessibility of all nodes in the road network. The second step uses stochastic user equilibrium and system optimum assignment models to assign the traffic distributed between all possible origins and destinations to the road network itself. The analysis is done using TransCAD software ver. 4.5 and Network Analysis/Arc GIS 10.3 for Al-Amarah City. Results showed that in the base year, The results of the stochastic user equilibrium assignment model there is no link in Al-Amarah network has v/c ratio between the range (0.81-1.00) and over which represent level of service equal to or more than D. The overall evaluation of Al-Amarah road network is level of service (LOS) equal to B and there is an improvement in the work of Al-Amarah road network by using the system optimum model. In the target year, the results of two assignment models are proximally similar. The black color (v/c >1.0) is appeared clearly in the assignment result map especially on the two main bridges in the city (Al-Yugoslavi Bridge and Al-Jumhuriah Bridge). When the red color (LOS E) is appeared in the links in near the centers of sectors (1,2, and 4). The most links ranges between (LOS D and LOS C) with v/c ratio Range (0.70-0.90).

Impacts of time restriction on heavy truck corridors: The case study of Mexico City

This paper presents a macroscopic analysis of the impact that a time restriction policy for heavy trucks on the main access corridors in Mexico City has on traffic congestion and local pollutant emissions (NOx and CO). The time restriction is a heavy truck ban at the morning rush hour.Four groups of scenarios (15 scenarios) are analyzed, taking into consideration the reactions from carriers, which were obtained from a survey. These reactions are changes to truck paths, truck size and operational schedule (off-peak operations), and the use of consolidation centers. Some scenarios also consider road infrastructure improvement as a complementary policy. This paper describes data collection and statistical analysis, the O-D matrix obtained from each scenario and the traffic assignment process. The comparison of scenarios indicates that: (1) The time restriction policy for heavy trucks on the main access corridors has a negative impact on the whole city’s total congestion and local emissions; and (2) The implementation of consolidation centers beyond the area of restriction to available land without adequate road infrastructure does not reduce the impact of the time restriction policy.

Origin-Destination Estimation Using Probe Vehicle Trajectory and Link Counts

This paper presents two origin-destination flow estimation models using sampled GPS positions of probe vehicles and link flow counts. The first model, named as SPP model (scaled probe OD as prior OD), uses scaled probe vehicle OD matrix as prior OD matrix and applies conventional generalized least squares (GLS) framework to conduct OD correction using link counts; the second model, PRA model (probe ratio assignment), is an extension of SPP in which the observed link probe ratios are also included as additional information in the OD estimation process. For both models, the study explored a new way to construct assignment matrices directly from sampled probe trajectories to avoid sophisticated traffic assignment process. Then, for performance evaluation, a comprehensive numerical experiment was conducted using simulation dataset. The results showed that when the distribution of probe vehicle ratios is homogeneous among different OD pairs, both proposed models achieved similar degree of improvement compared with the prior OD pattern. However, under the case that the distribution of probe vehicle ratios is heterogeneous across different OD pairs, PRA model achieved more significant reduction on OD flow estimations compared with SPP model. Grounded on both theoretical derivations and empirical tests, the study provided in-depth discussions regarding the strengths and challenges of probe vehicle based OD estimation models.

DESAIN GEOMETRI PERSIMPANGAN DAN AKSES KAMPUS UNIVERSITAS KRISTEN PETRA

Congestion that occurred in the vicinity of Petra Christian University campus tends to increasing on the peak hours. One of the possible solutions to reduce congestion is to revamp the geometric design of campus access, as proposed in the final report of PCU Master Plan. The study was conducted to determine differences in the intersection performance indicator between the existing conditions compared with the proposed access design. The data was collected through survey of road inventory, origin-destination, and turning movement. The traffic assignment process carried using Trafikplan software, and analysis of the access performed by using software KAJI. Based on the result, it can be inferred if the proposed design of geometric access implemented, the intersection capacity estimated to be increase up to 46.6%, degree of saturation reduce up to 31.9%, intersection delay reduce up to 57%, and queue probability reduce up to 50.9%; compared with the existing access conditions till the year 2020. The demand management program should also be considered as an option to reduce congestion. Keywords: unsignalised intersection, reversible lane

Influence of Capacity Restraint Functions on Traffic Distribution and Their Practical Use in the Traffic Modelling

The article deals with the issue of capacity restraint functions (curves) determining for roads and their use in the transport model. It describes the procedure for obtaining traffic flow characteristics and the derivation of the parameters needed to create basic restraint BPR function (Bureau of Public Roads). New parameters were then used in traffic assignment process. The impact of parameter changes for routes redistribution on traffic network was evaluated in detail.The practical use of BPR function in the real traffic model is described in the second part. The article presents the method of volume-delay function definition for different model links types.

Computation of Equilibrium Distributions of Markov Traffic-Assignment Models

Markov traffic-assignment models explicitly represent the day-to-day evolving interaction between traffic congestion and drivers' information acquisition and choice processes. Such models can, in principle, be used to investigate traffic flows in stochastic equilibrium, yielding estimates of the equilibrium mean and covariance matrix of link or route traffic flows. However, in general these equilibrium moments cannot be written down in closed form. While Monte Carlo simulations of the assignment process may be used to produce “empirical” estimates, this approach can be extremely computationally expensive if reliable results (relatively free of Monte Carlo error) are to be obtained. In this paper an alternative method of computing the equilibrium distribution is proposed, applicable to the class of Markov models with linear exponential learning filters. Based on asymptotic results, this equilibrium distribution may be approximated by a Gaussian process, meaning that the problem reduces to determining the first two multivariate moments in equilibrium. The first of these moments, the mean flow vector, may be estimated by a conventional traffic-assignment model. The second, the flow covariance matrix, is estimated through various linear approximations, yielding an explicit expression. The proposed approximations are seen to operate well in a number of illustrative examples. The robustness of the approximations (in terms of network input data) is discussed, and shown to be connected with the “volatility” of the traffic assignment process.

Accuracy and Performance of Improved Speed-Flow Curves

Skabardonis and Dowling recommended updated Bureau of Public Road speed-flow curves for freeways and signalized arterials to improve the accuracy of speed estimates used in transportation demand models. These updated curves generally involved the use of higher power functions that show relatively little sensitivity to volume changes until demand exceeds capacity, when the predicted speed drops abruptly to a very low value. Skabardonis and Dowling demonstrated that the curves provide improved estimates of vehicle speeds under both uncongested and queueing conditions; however, they did not investigate the impact of these curves on the performance of travel demand models. Practitioners have been concerned about the impacts of such abrupt speed-flow curves on the performance of their travel demand models. Spiess has stated that higher power functions are more difficult computationally for computers to evaluate and that more abrupt speed-flow curves adversely affect the rate of convergence to equilibrium solutions in the traffic assignment process. In this paper the impact of the Skabardonis and Dowling updated speed-flow curves on the performance of selected travel demand models is investigated. The updated speed-flow curves were found to significantly increase travel demand model run times. However, it is demonstrated that an alternative speed-flow equation developed by Akçelik has similar or better accuracy and provides much superior convergence properties during the traffic assignment process. The Akçelik curve significantly reduced travel demand model run times.

Implementation of Highway Capacity Manual–Based Volume-Delay Functions in Regional Traffic Assignment Process

Recent model development efforts for the travel demand forecasting process have focused on increasing the consistency and reasonableness of the results. A major target of these efforts has been the traffic assignment process and, more specifically, improving the estimates of congested travel speeds resulting from traffic assignments. A number of studies and enhancements have centered on the volume-delay functions used for the assignment process. Many of these studies have used the workhorse volume-delay function, the BPR function, and have investigated modifications to the function coefficients. In its recent update of the Albuquerque Area Travel Models, the Middle Rio Grande Council of Governments successfully implemented a regional traffic assignment process based on capacity estimation techniques and volume-delay functions documented in the Highway Capacity Manual. The assignment procedures developed for the Albuquerque area resulted in volume-delay functions that produced accurate traffic volumes while producing accurate congested speeds on the network. The capacity estimation procedures increase the realism of the network representation and provide increased analysis flexibility. Commercially available travel demand modeling software has been used to implement the Highway Capacity Manual–based volume-delay functions.

Implementation ofHighway Capacity Manual-Based Volume-Delay Functions in Regional Traffic Assignment Process

Recent model development efforts for the travel demand forecasting process have focused on increasing the consistency and reasonableness of the results. A major target of these efforts has been the traffic assignment process and, more specifically, improving the estimates of congested travel speeds resulting from traffic assignments. A number of studies and enhancements have centered on the volume-delay functions used for the assignment process. Many of these studies have used the workhorse volume-delay function, the BPR function, and have investigated modifications to the function coefficients. In its recent update of the Albuquerque Area Travel Models, the Middle Rio Grande Council of Governments successfully implemented a regional traffic assignment process based on capacity estimation techniques and volume-delay functions documented in the Highway Capacity Manual. The assignment procedures developed for the Albuquerque area resulted in volume-delay functions that produced accurate traffic volumes whil...