prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What position did Simon English hold in the 2008 general election?

Read the following text and answer briefly.

Context 1: Sir Simon William English  (born 30 December 1961) is a New Zealand former National Party politician who served as the 39th prime minister of New Zealand from 2016 to 2017  He had previously served as the 17th deputy prime minister of New Zealand and minister of finance from 2008 to 2016 under John Key and the Fifth National Government 

A farmer and public servant before entering politics, English was elected to the New Zealand Parliament in  as the National Party's candidate in the Wallace electorate  He was elevated to Cabinet in 1996 and in 1999 was made minister of finance, although he served for less than a year due to his party's loss at the 1999 general election  In October 2001, English replaced Jenny Shipley as the leader of the National Party (and consequently as Leader of the Opposition)  He led the party to its worst defeat at the 2002 general election, and as a consequence, in October 2003 he was replaced as leader by Don Brash 

In November 2006, after Brash's resignation, English became deputy leader under John Key  After National's victory at the 2008 general election, he became deputy prime minister and was also made minister of finance for the second time  Under English's direction New Zealand's economy maintained steady growth during National's three terms of government  He became a list-only MP after stepping down as an electorate MP at the 2014 general election 

John Key resigned as leader of the National Party and prime minister in December 2016  English won the resulting leadership election unopposed and was sworn in as prime minister on 12 December 2016  His tenure was only ten months, and included a three-month election campaign  In the 2017 general election, National won the largest number of seats but fell short of a majority  The parties holding the balance of power declined to support the existing government, and English was subsequently replaced as prime minister by Jacinda Ardern, leader of the Labour Party  English initially continued on as Leader of the Opposition, but resigned as leader of the National Party on 27 February 2018 and left parliament two weeks later 

Early life
English was born on 30 December 1961 at Lumsden Maternity Centre in Lumsden  He is the eleventh of twelve children of Mervyn English and Norah (née O'Brien) English  His parents purchased Rosedale, a mixed sheep and cropping farm in Dipton, Southland from Mervyn's uncle, Vincent English, a bachelor, in 1944  English was born in the maternity unit at Lumsden 

English attended St Thomas's School in Winton, then boarded at St  Patrick's College in Upper Hutt, where he became head boy  He played in the first XV of the school's rugby team  English went on to study commerce at the University of Otago, where he was a resident at Selwyn College, and then completed an honours degree in English literature at Victoria University of Wellington 

After finishing his studies, English returned to Dipton and farmed for a few years  From 1987 to 1989, he worked in Wellington as a policy analyst for the New Zealand Treasury, at a time when the free market policies favoured by Labour's finance minister Roger Douglas (known collectively as "Rogernomics") were being implemented 

English joined the National Party in 1980, while at Victoria University  He served for a period as chairman of the Southland branch of the Young Nationals, and became a member of the Wallace electorate committee  After moving to Wellington, he served for periods on the Island Bay and Miramar electorate committees, respectively 

Fourth National Government (1990–1999)

At the 1990 general election, English stood as the National candidate in Wallace, replacing the retiring Derek Angus, and was elected with a large majority  He would hold this seat, renamed Clutha-Southland in 1996, until 2014  He and three other newly elected National MPs (Tony Ryall, Nick Smith, and Roger Sowry) were soon identified as rising stars in New Zealand politics, and at various points were dubbed the "brat pack", the "gang of four", and the "young Turks"  In his first term in parliament, English chaired a select committee into social services  He was made a parliamentary under-secretary in 1993, serving under the Minister of Health 

First period in cabinet (1996–1999)
In early 1996, English was elevated to cabinet by Prime Minister Jim Bolger, becoming the Minister for Crown Health Enterprises and Associate Minister of Education (to Wyatt Creech)  He was 34 at the time, becoming the cabinet's youngest member  After the 1996 general election, the National Party was forced into a coalition with New Zealand First to retain government  In the resulting cabinet reshuffle, English emerged as Minister of Health  However, as a condition of the coalition agreement, NZ First's Neil Kirton (a first-term MP) was made Associate Minister of Health, effectively becoming English's deputy  This arrangement was described in the press as a "shotgun marriage", and there were frequent differences of opinion between the two ministers  After their relationship became unworkable, Kirton was sacked from the role in August 1997, with the agreement of NZ First leader Winston Peters 

As Minister of Health, English was responsible for continuing the reforms to the public health system that National had begun after the 1990 general election  The reforms were unpopular, and health was perceived as one of the government's weaknesses, with the health portfolio consequently being viewed as a challenge  English believed that the unpopularity of the reforms was in part due to a failure in messaging, and encouraged his National colleagues to avoid bureaucratic and money-focused language (such as references to "balance sheets" and "user charges") and instead talk about the improvements to services the government's reforms would bring  He also rejected the idea that public hospitals could be run as commercial enterprises, a view which some of his colleagues had previously promoted 

By early 1997, as dissatisfaction with Bolger's leadership began to grow, English was being touted as a potential successor, along with Jenny Shipley and Doug Graham  His age (35) was viewed as the main impediment to a successful leadership run  National's leadership troubles were resolved in December 1997, when Bolger resigned and Shipley was elected to the leadership unopposed  English had been a supporter of Bolger as leader, but Shipley reappointed him Minister of Health in her new cabinet 

English was promoted to Minister of Finance in a reshuffle in January 1999, a position which was at the time subordinate to the Treasurer, Bill Birch  After a few months, the pair switched positions as part of Birch's transition to retirement, with English assuming the senior portfolio  In early interviews, he emphasised his wish to be seen as a pragmatist rather than an ideologue, and said that the initiatives of some of his predecessors (Roger Douglas's "Rogernomics" and Ruth Richardson's "Ruthanasia") had focused on "fruitless, theoretical debates" when "people just want to see problems solved" 

Opposition (1999–2008)

After the National Party lost the 1999 election to Helen Clark's Labour Party, English continued on in the shadow cabinet as National's spokesperson for finance  He was elected deputy leader of the party in February 2001, following the resignation of Wyatt Creech, with Gerry Brownlee being his unsuccessful opponent 

Leader of the Opposition
In October 2001, after months of speculation, Jenny Shipley resigned as leader of the National Party after being told she no longer had the support of the party caucus  English was elected as her replacement unopposed (with Roger Sowry as his deputy), and consequently became Leader of the Opposition  However, he did not openly organise against Shipley, and according to The Southland Times "there was almost an element of 'aw, shucks, I'll do it then' about Mr English's ascension" 

Aged 39 when he was elected, English became the second-youngest leader in the National Party's history, after Jim McLay (who was 38 when elected in 1984)  He also became only the third Southlander to lead a major New Zealand political party, after Joseph Ward and Adam Hamilton  However, English failed to improve the party's performance  In the 2002 election, National suffered its worst electoral defeat ever, gaining barely more than twenty percent of the vote  English described it as "the worst day of my political life"  Both party insiders and the general public were split as to how much to blame him for the loss, but most of the party believed that English would be able to rebuild National's support 

By late 2003, however, National's performance in opinion polls remained poor  The party had briefly increased its popularity in the year following the election, but by October its support had fallen to levels only slightly better than what it achieved in the last ballot  English also appeared in a boxing match for a charity against entertainer Ted Clarke  This did not boost his polling or that of the National party either, with suggestions that it devalued his image as a serious politician  Don Brash, former governor of the Reserve Bank and a relative newcomer to politics, began to build up support to replace English  On 28 October, Brash gained sufficient backing in Caucus to defeat English in a leadership contest 

Shadow cabinet roles and deputy leader
On 2 November 2003, when Brash changed responsibilities for certain MPs, English became National's spokesman for education, ranked at fifth place in the party's parliamentary hierarchy  He remained in parliament after the 2005 election  In his new shadow education portfolio, English performed strongly, and remained a party favourite despite his election defeat as leader in 2002, eventually being returned to the finance portfolio in August 2004 as deputy spokesman (while still retaining responsibility for education) 

In November 2006, Brash resigned as leader  English was considered as a potential replacement leader (running against John Key) or deputy leader (against incumbent Gerry Brownlee) in the ensuing leadership election  However, a contest was avoided when the MPs agreed a Key/English ticket would run unopposed in a display of party unity  English took over the deputy leadership and the finance portfolio in the Key shadow cabinet 

Fifth National Government (2008–2017)

Deputy Prime Minister and Minister of Finance (2008–2016)

At the 2008 election, English was re-elected by his electorate, winning by a margin of about 15,500 votes  He became Deputy Prime Minister of New Zealand and Minister of Finance in the fifth National Government, being sworn into office on 19 November 2008 and continued to serve in those roles until becoming Prime Minister on 12 December 2014  He was also made Minister of Infrastructure in National's first term of government and Minister responsible for Housing New Zealand Corporation and minister responsible for the New Zealand flag consideration process in its third 

He was comfortably re-elected in Clutha-Southland in the 2011 election but opted to run as a party-list candidate in 2014  

The pairing of John Key as leader of the National Party and English as his deputy has been compared to that of Bob Hawke and Paul Keating (in Australia) and Tony Blair and Gordon Brown (in the UK) 

English acceded to the role of Finance Minister in the continuing wake of the financial crisis  In response to New Zealand's rising debt, English made budget deficit-reduction his main priority  His first budget outlined three focuses in New Zealand's financial recovery: "improving the business environment and removing roadblocks to growth; investment in productive infrastructure; and improving the way government works"  One of his first acts was creating the National Infrastructure Unit, charged with formulating a plan for infrastructure projects and investments  He commissioned a government-wide spending review, with an aim to reducing government expenditure—with the exceptions of a two-year stimulus package and long-term increases on infrastructure spending 

In April 2011, the Opposition criticised English for suggesting that New Zealand businesses could use New Zealand's low wages to help it compete with Australia  The National Government campaigned for re-election in 2011 on its economic record  The Government boasted growth for five consecutive quarters up to mid-2010, totalling 1 6% of real GDP 

Strong growth resulted in a surplus of $473 million for the 2015/16 financial year, projected to rise to $8 5 billion by 2020/21  In his 2016 Economic and Fiscal Update address, English stated that reducing debt and tackling the costs of the 2016 Kaikōura earthquake were higher priorities than reducing rates of tax 

Allowances issue
In 2009, the media, including TVNZ and TV3 revealed that English was receiving about NZ$900 a week as part of a living allowance for ministers, to live in his own NZ$1 2 million Wellington home  At the time, English also received $276,200 in his annual salary as Deputy Prime Minister  It was also revealed other ministers with homes in the capital city were also claiming accommodation allowances  On 3 August 2009, Prime Minister John Key started a review of the housing allowances claimed by cabinet ministers  English subsequently paid back $12,000 and only claimed about $24,000 a year in living allowances  The Auditor-General's office said in September 2009 that they were making "preliminary enquiries" into parliamentary housing expenses in response to a letter of complaint from Progressive party leader Jim Anderton  Two days later English stated that he would no longer take up any housing allowance and had paid back all the allowance he had received since the November 2008 election 

Prime Minister (2016–2017)

John Key resigned on 12 December, and endorsed English as his successor in the resulting leadership election  Following the drop-out of both Judith Collins and Jonathan Coleman from the leadership election, English was sworn in as the 39th Prime Minister of New Zealand on 12 December 2016 

English appointed his first cabinet on 18 December  In a reshuffle, he appointed Steven Joyce to succeed him as Finance Minister, while most ministerial portfolios remained the same 

In February 2017, English did not attend Waitangi Day commemorations at the historic treaty grounds, reportedly in response to the Ngāpuhi iwi's decision to stop the Prime Minister from speaking at the marae  Ngāpuhi have protested the Government's negotiation of the Trans Pacific Partnership Agreement (TPPA), which the iwi believe infringes upon Māori sovereignty, and thus does not adhere to the Treaty of Waitangi  English had been invited to attend in an official capacity; his non-attendance was criticised by a Ngāpuhi elder and Opposition leader Andrew Little 

In his first overseas trip as Prime Minister, English travelled to Europe to discuss trade ties, including a prospective New Zealand–European Union free trade agreement  He first travelled to London on 13 January 2017 to meet British Prime Minister Theresa May  Discussing trade relations, English said the two nations were "natural partners" and would "continue to forge ties" after the UK's withdrawal from the EU  He also arranged to meet with London Mayor Sadiq Khan, Belgian Prime Minister Charles Michel and German Chancellor Angela Merkel   In a meeting with Merkel, English received crucial backing from Germany for a trade deal with the EU  On 16 January, English stated that his government would continue to promote TPPA, despite the United States' decision to withdraw from the agreement  He explained that Southeast Asian countries would now be treated as a priority in negotiations—he also asserted that the United States was ceding influence to China by its rejection of the trade pact 

At a press conference at the Beehive on 1 February 2017, English announced that the 2017 general election would be held on 23 September  The Prime Minister later confirmed that his party would approach ACT, United Future and the Māori Party if confidence and supply agreements were required to form a government following the election  In his second cabinet reshuffle on 24 April, English appointed Gerry Brownlee as his new Foreign Affairs Minister; he also promoted Nikki Kaye to the portfolio of Education Minister, and moved Mark Mitchell into the cabinet to become Defence Minister  The reshuffle was perceived as an election preparation 

On 13 February 2017, English welcomed Australian Prime Minister Malcolm Turnbull to Wellington  The two leaders reaffirmed their shared trade agenda, and discussed changes to the Australian citizenship pathway which will affect permanent residents originating from New Zealand 

On 19 June, it was reported that Todd Barclay, who succeeded English as MP for Clutha-Southland, had clandestinely recorded one of his employee's conversations the previous year, and that John Key's leaders' budget was used to pay a confidential settlement after the employee resigned  English admitted that he had been aware of the illegal recording and the settlement, and thus implicated in the scandal 

During the 2017 National campaign launch, English introduced a $379 million social investment package including digital learning academies for high school students, more resources for mathematics, and boosting support for teaching second languages in schools, and maintaining National Standards in the school curriculum  Prime Minister English also sought to defend National's financial management and economic track record and claimed that the opposition Labour Party would raise taxes  Early opinion polling had forecast a poor showing in the election for the Labour Party, but in early August 37-year-old Jacinda Ardern took over as Labour leader and seemingly energised younger voters 

At the 2017 general election, National won the largest share of the party vote (44 4%) and the largest number of seats (56) in the House Representatives  However, National lacked enough seats to govern alone due to two of the party's support partners, the Māori Party and United Future, losing their parliamentary seats  In response, English stated that the party would be entering into talks to form a coalition with New Zealand First  Following talks with the two largest parties, New Zealand First entered a coalition arrangement with the Labour Party  English was succeeded as prime minister by Jacinda Ardern on 26 October 

Opposition (2017–2018)

Leader of the Opposition
English was re-elected as National Party leader on 24 October 2017  At the time of his re-election, English announced his intention to stay on as leader until the next general election  On 13 February 2018, however, he stood down as National Party leader due to personal reasons, and instructed the party to put into motion the processes to elect a new leader  He also retired from Parliament  English's resignation followed weeks of speculation that he would step aside for a new leader  On 27 February, he was succeeded as party leader by Simon Bridges as the result of the leadership election held that day 

Post-premiership 
In 2018, English joined the board of Australian conglomerate, Wesfarmers  English serves in Chairmanships of Mount Cook Alpine Salmon, Impact Lab Ltd and Manawanui Support Ltd  He is also a director of The Instillery, Centre for Independent Studies and The Todd Corporation Limited, and is a member of the Impact Advisory Group of Macquarie Infrastructure and Real Assets 

Political and social views

English is regarded as more socially conservative than his predecessor, John Key  He has stated his opposition to voluntary euthanasia and physician-assisted suicide, same-sex civil unions, and the decriminalisation of prostitution  As Prime Minister he opposed any "liberalisation" of abortion law 

In 2004, English voted against a bill to establish civil unions for both same-sex and opposite-sex couples  In 2005, he voted for the Marriage (Gender Clarification) Amendment Bill, which would have amended the Marriage Act to define marriage as only between a man and a woman  English voted against the Marriage (Definition of Marriage) Amendment Bill, a bill that legalised same-sex marriage in New Zealand  However, in December 2016 he stated, "I'd probably vote differently now on the gay marriage issue  I don't think that gay marriage is a threat to anyone else's marriage" 

In 2009, English voted against the Misuse of Drugs (Medicinal Cannabis) Amendment Bill, a bill aimed at amending the Misuse of Drugs Act so that cannabis could be used for medical purposes 

Personal life 
English met his future wife, Mary Scanlon, at university  She was studying medicine at the time, and became a general practitioner  Both her parents were immigrants, her father being Samoan and her mother Italian, born on the island of Stromboli  They have six children: a daughter and five sons 

English is a practising Roman Catholic, but has stated that he considers his religious beliefs personal and thus separate from politics 

In June 2002, English took part in TV3's Fight For Life, a celebrity boxing fundraiser to raise money for the Yellow Ribbon anti-youth-suicide campaign, influenced by the death of a teenage nephew in 1997  He lost a split decision to former university colleague Ted Clarke 

Honours
In the 2018 Queen's Birthday Honours, English was appointed a Knight Companion of the New Zealand Order of Merit, for services of over 27 years to the State 

See also

List of New Zealand governments
Politics of New Zealand

References

External links

Profile at National Party 
Profile on Parliament nz
Releases and speeches at Beehive govt nz

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1961 births
21st-century New Zealand politicians
Candidates in the 2017 New Zealand general election
Deputy Prime Ministers of New Zealand
Leaders of the Opposition (New Zealand)
Living people
Members of the Cabinet of New Zealand
Members of the New Zealand House of Representatives
New Zealand farmers
New Zealand finance ministers
New Zealand list MPs
New Zealand MPs for South Island electorates
New Zealand National Party MPs
New Zealand National Party leaders
New Zealand Roman Catholics
New Zealand people of Irish descent
People educated at St  Patrick's College, Silverstream
People from Dipton, New Zealand
People from Lumsden, New Zealand
Prime Ministers of New Zealand
University of Otago alumni
Victoria University of Wellington alumni
Knights Companion of the New Zealand Order of Merit
New Zealand politicians awarded knighthoods
Context 2: Paper Info

Title: SLAS: Speed and Lane Advisory System for Highway Navigation
Publish Date: Unkown
Author List: Faizan Tariq, David Isele, John Baras, Sangjae Bae

Figure

Fig  1 Motivational Example With a slow moving vehicle ahead, the ego vehicle (in blue) may decide to either change lane to the fast moving lane (left) to minimize travel time or adjust its speed without changing lanes to preserve safety but it would be unwise for it to switch to the slow moving lane (right) as that would not benefit travel time or safety 
Fig  3  Simulation Setup Scenario Runner sets up the scenario for the CARLA Simulator, which then communicates with the SLAS and the Planning and Control ROS (Robot Operating System) nodes through the ROS bridge node 
Fig  4  Testing scenario with three lanes: lane 0 (left), lane 1 (center) and lane 2 (right) The expected motion of the ego vehicle, over the course of the simulation, is shown with numbered frames The right most lane (lane 3) is reserved for merging traffic so it is not utilized in our simulation 
Fig  5  Left: Travel time comparison Center: Lane choice (lateral position) comparison The center lines of lanes 0 (left), 1 (center) and 2 (right) have fixed lateral displacements of 0m, 3 5m and 7m respectively Right: Headway comparison With no leading vehicle, the headway is restricted by the visibility range of 50m 

abstract

This paper proposes a hierarchical autonomous vehicle navigation architecture, composed of a high-level speed and lane advisory system (SLAS) coupled with low-level trajectory generation and trajectory following modules  Specifically, we target a multi-lane highway driving scenario where an autonomous ego vehicle navigates in traffic 
We propose a novel receding horizon mixed-integer optimization based method for SLAS with the objective to minimize travel time while accounting for passenger comfort  We further incorporate various modifications in the proposed approach to improve the overall computational efficiency and achieve real-time performance 
We demonstrate the efficacy of the proposed approach in contrast to the existing methods, when applied in conjunction with state-of-the-art trajectory generation and trajectory following frameworks, in a CARLA simulation environment 

INTRODUCTION

Lane changing is considered to be one of the most risky driving behaviors since it is highly contingent upon multimodal trajectory predictions of neighboring vehicles and requires timely decision making   It is further influenced by a number of uncertainty factors such as road conditions, measurement accuracy, and a long tail of behavioral uncertainty of on-road agents 
However, if executed efficiently, lane changing coupled with speed adjustment can yield significant improvement in minimizing overall travel time while ensuring passenger comfort   To elaborate further, consider the scenario presented in Fig    Based on the predicted motion (shown in a lighter shade) of the neighboring vehicles (shown in orange), the ego vehicle (shown in blue) may decide to either change lane left in an attempt to minimize its travel time or slow down in the current lane to maintain safety 
However, it would be imprudent for the ego vehicle to risk changing lane right and consequently get stuck behind a slow moving vehicle even though there is presently a greater headway  This simple scenario highlights the importance of foresight and long planning-horizon in strategic decision making for autonomous vehicles 
Existing methods like MOBIL give us the ability to change lanes but behave greedily (prioritizing immediate rewards) oftentimes, which can lead to sub-optimal performance  It was shown in that the lane changing performance can be improved with an A inspired approach, but the formulation was limited to constant speed 
Such an approach is unable to assess the benefits of speed adjustment 1 University of Maryland, College Park, MD, USA  Email: {mftariq,baras}@umd edu  2 Honda Research Institute, San Jose, CA, USA  Email: {disele,sbae}@honda-ri com  Research supported by Honda Research Institute, USA   in minimizing overall travel time 
As will become apparent in Section IV, it may be necessary at times to sacrifice on shortterm benefits to gain long-term performance improvements  In such a scenario, an approach with speed adjustment coupled with long planning horizon has the foresight to deliver significantly better results  Moreover, the inclusion of speed adjustment in the decision making process inhibits the risk of incurring trajectory infeasibility as the environment conditions may prevent the ego vehicle from traveling at a constant reference speed and the low-level planner may be unable to handle such a discrepancy 
Therefore, in this work, we propose a low complexity receding horizon optimization based approach that outputs the lane change maneuvers coupled with speed adjustments for long planning horizons (> 15s) while guaranteeing safety  The long horizon strategic decision making gives ego vehicle the ability to proactively anticipate and handle challenging driving situations 
Literature review: In the literature, speed and lane changing decisions are generally considered from a motion planner's perspective , which allows for a simultaneous determination of target lanes and waypoints to perform the maneuver  The motion planning methods present in the literature can broadly be categorized into sampling-based, learning-based and optimization-based approaches 
In regards to the sampling-based approaches, single-query methods, in particular the different variants of RRT, are preferred over multi-query methods, like roadmap-based methods, due to the faster execution time and their ability to incorporate non-holonomic constraints   Even though these methods are able to incorporate safety guarantees by sampling feasible trajectories from a reachable safe set , the overall driving experience is often rather uncomfortable due to the concatenation of individual trajectories 
Moreover, the asymptotic optimality guarantees availed by arXiv:2303 00861v1 [cs RO] 1 Mar 2023 these methods do not help with real-world implementation in complex driving scenarios since they tend to have high sample complexity   In terms of the learning-based methods, the preferred approach seems to be the variations of Reinforcement Learning techniques applied in a simulated environment , , , ,  
These approaches, although seeming to work well in simulation, have concerns regarding real-world implementation due to the large amount of training data that they require, the exploration of unsafe behaviors during training, and a general inability to handle edge cases  They mainly utilize neural networks as function approximators which yields low computational complexity but also results in a lack of explainability and safety guarantees 
Lastly, the optimization-based approaches, especially the derivatives of optimal control methods, are abundant in the literature  In contrast to the potential-field based approaches that yield decent collision avoidance performance but are unable to accommodate vehicle dynamics, the optimal control methods , especially the derivatives of Model Predictive Control (MPC) approach , , , yield excellent collision avoidance performance while accommodating vehicle dynamics 
However, this performance comes at a cost of high computational complexity, arising mainly from the non-convex collision avoidance and the non-linear dynamics constraints  This, in turn, restricts the planning horizon to merely a few seconds  The key requirements for the algorithmic design of an autonomous vehicle include real-time operation, safety guarantees, optimality with respect to some metric(s), and accounting for the behavior variability of on-road agents 
Considering these requirements, we propose an optimization-based behavioral planning framework that enables autonomous vehicle maneuvering on multilane highways  While having the benefits of optimizationbased approaches, our method achieves a low computational complexity by employing a binary representation of the decoupled lane indicator dynamics in lieu of lateral dynamics, and utilizing algorithmic modifications to aid numerical computations 
Specifically, our method provides: • optimality with respect to travel time and comfort; • safety and feasibility guarantees; • real-time applicability for a long planning horizon; and • modularity in design, which enables the integration of external trajectory prediction modules  The proposed method fills in the research gap by meeting all the key algorithmic requirements while simultaneously gaining the foresight to make strategic decisions that yield long-term performance benefits, as verified in Section IV 
In this section, we present the algorithmic pipeline and formalize the road, observation and vehicle dynamics models that will be utilized in the subsequent sections 

Algorithmic Pipeline

Fig  illustrates the algorithmic pipeline of the proposed navigation architecture, in reference to the various existing   Algorithmic pipeline of the proposed navigation architecture  The raw sensory input data is processed by the Perception, and Simultaneous Localization and Mapping (SLAM) modules to place the autonomous vehicle relative to the various environmental entities in a unified frame of reference 
This information is then passed on to the navigation stack, composed of the behavioral planning, motion planning, and vehicle control modules  The output of the navigation module is passed down further in terms of actuation commands (brake, throttle and steering) to the actuators  algorithmic modules deployed on an autonomous vehicle 
The taxonomy of the various components of the navigation stack (highlighted by the dotted rectangle) is borrowed from   This pipeline essentially improves the pipeline introduced in by adding a speed advisory system  Our main focus is the development of the behavior planning module, highlighted as SLAS in Fig    SLAS outputs the target lane and reference speed which are utilized by the motion planning module to generate a reference trajectory for the ego vehicle 
The vehicle controllers compute the throttle and steering commands to track the trajectory accordingly  For the motion planning module, we adopt the Neural Networks integrated Model Predictive Control (NNMPC) due to its ability to accommodate the behaviors of neighboring vehicles in the trajectory generation process 
In our approach, we assume that the perception (of other vehicles) and the localization (of ego vehicle) are known without any uncertainty, for simplicity, but the modular architecture avails us the ability to integrate any perception or SLAM module in the overall framework  Throughout the manuscript, Z will denote the set of integers and R the set of real numbers 
For some a, c ∈ Z and a < c, we will write For some e, g ∈ R and e < g, we will write

Road Model

The physical road structure is modeled as a continuous multi-lane highway with negligible curvature and unidirectional traffic flow  The lanes on the highway are clearly demarcated and at any given time k, the number of available lanes for the vehicles to travel on is denoted by N l (k) while the road speed limit is denoted by V l  
Therefore, the set of lanes available for traveling at a given time instant k is denoted by   We work with the Frenet coordinate system where the distance along the road is denoted by the longitudinal displacement (s) and the distance perpendicular to the road is defined by the lateral displacement (d) 
Each lane is assigned a lane indicator variable l  The leftmost lane, with respect to the direction of traffic flow, is assigned a value of l = 0 while each subsequent lane is assigned an increasing integer value for l, as depicted in Fig   

Vehicle Model

Since we aim to have real-time computations for a long planning horizon (> 15s), we model the vehicle dynamics with a linearized decoupled dynamical system  For the highway driving scenario, where the road curvature is typically small, it is reasonable to assume a decoupling between the lateral and the longitudinal dynamics , especially for the behavior planning layer 
Therefore, we utilize a linear constant acceleration model for the longitudinal dynamics and abstract out the lateral dynamics with a lane indicator variable  For the lane change dynamics, we use a moving average filter coupled with a rounding function to model the time required by the ego vehicle to change lanes 
This is compactly represented as: where s 0 (k), v 0 (k), l 0 (k) and L(k) denote the ego vehicle's longitudinal displacement, speed, lane indicator and target lane, respectively, at time instant k; the subscript i indexes the vehicles on the road with 0 being reserved for the ego vehicle; T s denotes the discretization time step; and N corresponds to the number of time steps required to change lane 
The state (x 0 (k)) and control input (u 0 (k)) to the system at time instant k are defined as: where V m denotes the maximum speed of the ego vehicle 

Observation Model

For practical considerations, we restrict the ego vehicle's visibility range to the sensory perception limit, denoted by R v   Then, the set of vehicles in ego vehicle's visibility range at time instant k, represented by O(k), is defined as: where s i (k) corresponds to the longitudinal displacement of the observed vehicle 
Remark 1: For the multi-lane highway driving scenario, occlusion does not play a prominent role so we do not account for it in the existing formulation  However, the proposed framework can easily accommodate occlusion and measurement uncertainties since the receding horizon approach bases its decision on the most up-to-date information available at any given time, as demonstrated in  
In this section, we describe the prediction model to generate the predicted future trajectories of observed vehicles and present a discussion on the proposed receding horizon optimization-based behavioral planning module 

Trajectory Prediction

Reliable behavior and trajectory prediction of other traffic participants is crucial for safe maneuvering of autonomous vehicles  The algorithm proposed in Section III-B is able to incorporate any generic prediction module available in the literature as long as it can provide a deterministic predicted future trajectory for a given vehicle 
In this work, we formulate a low-complexity prediction model that highlights the flexibility and efficiency of our proposed approach  For an observed vehicle i ∈ O(k), the future speed profile is predicted using a piece-wise linear function while the lane profile is assumed to stay constant for the duration of the prediction horizon 
At a given time step k, the estimated acceleration (ā k i ) and the estimated speed (v k i ) parameters are obtained through linear regression with mean-squared error on the past o k i > 1 speed observations  Based on the estimated parameters, we predict the future speed and longitudinal displacement as follows:
Here, H a corresponds to the acceleration horizon while vk i (j) and ŝk i (j) respectively represent the predicted speed and longitudinal displacement for vehicle i, j time steps into the future starting from the current time instant k  Remark 2: Due to the modular nature of the proposed framework, the behavior planning module detailed in Section III-B can work with advanced maneuver-based (e g 
Markov Chain ) and interaction-based (e g  Social Generative Adversarial Networks ) trajectory prediction modules, allowing for interactive maneuvering behaviors 

Speed and Lane Advisory System

The goal of our behavior planning module, Speed and Lane Advisory System or in short, SLAS, is to determine a sequence of speed and lane change commands that would enable the ego vehicle to maximize its speed, thus minimizing the travel time, while accounting for driver comfort and abiding by its dynamical, actuator, and safety limits 
The output of this module is a relatively smooth speed and lane change profile which is then passed on to a motion planner  It is necessary to incorporate the dynamical and actuator limits in the behavioral planning module so as not to provide the motion planner with goals that are not reachable, and jeopardize the safety of the overall system as a result 
In the subsequent discussion, we provide a formulation of the optimization problem for SLAS; highlight the modifications necessary to improve the computational complexity; and, present safety and feasibility analysis  1) Optimization Problem with Integer Constraints: SLAS is posed as an optimization problem, with the objective to maximize speed while minimizing frequent lane changes and abrupt changes in speed 
The output of SLAS, at time instant k, is the control input u 0 (k + 1), as defined in   The optimization problem is formulated as follows: Objective Function: In the formulation above, the optimization variables are the ego vehicle's speed (v k (j)) and target lane (L k (j)), j step into the future, starting from time instant k 
Here, H corresponds to the planning horizon  The scalarization parameters γ 1 , γ 2 and γ 3 in the objective function account for a relative tradeoff between maximizing speed, minimizing lane changes and minimizing abrupt changes in speed respectively  Increasing γ 1 yields a more aggressive behavior with the priority placed on maximizing speed while γ 2 and γ 3 combine to place an emphasis on maximizing passenger comfort by reducing lane and speed changes respectively 
Dynamical Constraints: These constraints are put in place to ensure the dynamical feasibility of the solution  The constraints ( ), and serve to initialize the longitudinal displacement, speed and target lane respectively for the optimizer, based on the values observed at time instant k  The constraints and ( ) bound the ego vehicle's speed by the speed limit and the acceleration limits of the vehicle respectively 
The ego vehicle's speed is then used to calculate the projected longitudinal displacement in   The target lane values at any planning step (j) are restricted to the set of reachable values by ( ), ( ) and   Here, restricts the target lane to the set of available lanes (L(k)), ensures that the lane change, if needed, is made to the adjacent lane only and (17) models the time steps (N ) required for a lane change 
The flooring function can easily be transformed into a couple of linear constraints by the introduction of an auxiliary integer variable, as shown in the Appendix  Finally, l k (j) is merely the internal representation of the lane the ego vehicle is projected to travel on at planning step j  Safety Constraint: The safety constraint ensures that the ego vehicle maintains a minimum safe distance (L s i (j)) to the nearest vehicle i, in its projected lane of travel (l k (j)), at planning instant j 
We borrow the definition of this safe distance from , where the authors provide a formalization, based on the clause from Vienna Convention on Road Traffic that states that "A vehicle [   ] shall keep at a sufficient distance [   ] to avoid collision if the vehicle in front should suddenly slow down or stop "
Furthermore, the absolute value constraint can be decomposed into linear constraints by the application of big-M method and the introduction of an auxiliary variable, as shown in the Appendix  Remark 3: The proposed formulation can accommodate arbitrary number of lanes at any given time instant k  This means that if at any given time, the number of available lanes for traveling either increases or decreases, the proposed formulation will still continue to hold 
This is an important consideration since many a times on highways, some lanes are blocked due to various unanticipated situations such as road accidents, roadwork, narrowing of road etc  2) Computational Complexity Reduction: This section details the optimization problem reformulation with binary variables, optimization warm start technique and lazy constraint implementation, all of which combine to improve the computational complexity of our SLAS module 
Binary Variables: The proposed formulation in Section III-B has relatively high computation complexity (computation time of ∼ 2s in the worst case scenario -slow moving traffic blocking all the lanes) due to the integer decision variables yielding a mixed-integer optimization problem   To circumvent the computational overload, we reformulate the problem with binary variables that replace the integer variables, as follows:
where the Lk (i, j) represents the modified target lane variable, indexed by the lane (i) as well as the planning step (j) and Lk (a, b) = 1 represents the choice of lane a ∈ L as the target lane at planning step b ∈ Z   Then, some of the constraints from the SLAS formulation in Section III-B are modified as follows:
Here, initializes the target lane, (21) restricts the target lane at any planning step to the set of available lanes, restricts the lane change between consecutive planning steps to the adjacent lanes, and ( ) represents the augmented safety constraint  The implication ( =⇒ ) in ( ) can easily be transformed into a linear constraint (see Appendix) 
The augmented minimum safety distance ( Ls i (j)) incorporates the time required to execute the lane change maneuver (N ) from ( ) into the following unified safety constraint: where L l is the width of the lanes (see Fig  ), δ(k) is the signed lateral deviation of the ego vehicle from the previous target lane's boundary at time step k, and γ d (δ k (j)) is the dynamic cost of deviation from the previous target lane (L(k − 1)) 
Moreover, in the cost function , we take L k (0) = L(k − 1)  These costs are introduced to prevent the swerving (canceling of lane switch before completion) behavior, unless absolutely necessary (for safety purposes)  Remark 4: Since the ego vehicle is considered to have changed lane once it crosses a lane boundary, the deviation δ k (j) is considered from the lane boundary instead of the center of the target lane to maintain the continuity of γ d (δ k (j)) with respect to the lateral displacement of the ego vehicle 
Specifically, δ k (j) > 0 if the ego vehicle has crossed the previous target lane boundary and 0 otherwise  This is an important consideration since a discontinuity in γ d (δ k (j)), upon completion of lane change, may lead to infeasibility  Remark 5: The swerving behavior is suppressed but not completely eliminated with a hard constraint since such a behavior is necessary at times to react to the environment's unpredictability 
This reactive strategy, which is a distinctive feature of our approach, avails the algorithm the ability to proactively 'change its mind' in case something unanticipated happens in the environment that can jeopardize safety  Optimization Warm Start: To aid the optimizer in finding an initially feasible solution, we provide the solution from the previous time step as a reference 
Formally, This doesn't imply that the solution from time step k − 1 will hold exactly at time step k, owing to the unmodeled disturbances, but providing this reference aids the optimizer in finding an initially feasible solution in the vicinity of the reference solution  This observation is rooted in the premise that the solution for the long planning horizon is not expected to change significantly between time steps, given the sampling time is not too large, and the predicted behavior of on-road agents does not alter significantly 
It is also worth pointing out that the priority here is quickly finding a feasible solution that obeys the safety constraints and actuator limits, and recursively improving it rather than excessively iterating to reach at a global optimum  In our experiments, it was observed that a suboptimal solution was qualitatively not significantly different from the optimal one 
Therefore, we utilize the cutting planes method for optimization , which first looks for a feasible solution, using our provided reference, and then recursively updates it until either the globally minimal solution is found or the time limit is reached  Lazy Constraints: To further enhance the computational efficiency, we introduce a lazy implementation of the lane changing constraints  
It was observed in our experiments that a feasible solution without the lane changing constraints ( ) can be found several order of magnitude (∼ 10×) quicker than if we include these constraints so we decided to have a lazy implementation for them  With a lazy implementation , the solver finds a set of feasible solutions without the inclusion of these constraints and then determines the feasibility of those solutions from the reduced problem with respect to the lazy constraints 
3) Feasibility: By an argument similar to the one presented in , it is a relatively straightforward proof for recursive feasibility of the problem, i e  the optimization problem will continue to stay feasible, if initially feasible, with the trivial solution being matching the speed of the leading vehicle and not changing lanes 
In this section, we detail our experimental setup, demonstrate the performance of SLAS, and report a qualitative as well as a quantitative comparative analysis  The baselines in our comparative analysis are set to: Extended-Astar (EA ) , MOBIL , and no lane-change model (No-Change) 

Experimental Setup

The implementation setup, depicted in Fig  , is composed of the CARLA simulator (Version 0 9 11) , SLAS module (Section III-B), and the planner and controller module   To solve the optimization problem for SLAS, we use Gurobi Optimizer (Version 9 1 1)   The simulations are performed on a computer equipped with an Intel Xeon(R) CPU E5-2643 v4 @ 3 40GHz × 12 and NVIDIA Titan XP, running Ubuntu 20 04 LTS 
On average, the time required for each optimization step is ∼ 0 096s, while the maximum time limit for the optimizer is set to 0 2s, indicating the strong potential for real-time applicability 

Case Study

Figure illustrates the test case scenario for our comparative analysis  The scenario is composed of a highway segment with four lanes and the rightmost lane reserved for merging vehicles  The ego vehicle is initialized to follow a slow moving vehicle in lane 1 and has even slower moving traffic to its right in lane 2  Thus, the only option for it, in order to minimize travel time, is to switch left to lane 0 with faster moving traffic and greater headway 
Once it moves to lane 0, and overtakes the slow moving vehicle in lane 1, it has two options: either to keep traveling in lane 0 without making any lane change decisions until getting close to the lead vehicle or proactively exploiting the gap in lane 2 to switch to lane 3 in anticipation of traffic buildup in lanes 1 and 2  A strategic decision maker with foresight will choose to take the later option and make the decision proactively for a greater overall benefit 
The evaluation metrics for the comparative analysis include: travel time, lateral displacement, headway and distance to the closest vehicle  As for the simulation parameters, the simulation step size is set to 0 05s (simulation frequency of 20Hz); the velocities of vehicles in lanes 0, 1 and 2 are set to 8, 5 and 2 m/s respectively while the speed limit V l is set to 15m/s; the length of the highway patch is set to 350m while the width between the lanes is set to 3 5m; and the sensor visibility range is set to R v = 50m 
The parameters for SLAS are set as follows: T s = 0 4s, H = 40, N = 3, A min = −5m/s 2 , A max = 3 5m/s 2 , γ 1 = 1, γ 2 = 0 1 and γ 3 = 0 01  The values of these parameters can be tuned to yield an aggressive or defensive behavior of the algorithm  1) Travel Time: The left plot in Fig  depicts the travel time as a function of longitudinal displacement for the four algorithms 
As seen in the plot, our method (SLAS) maintains a lower overall travel time as compared to the other methods  Quantitatively speaking, SLAS outperforms EA , MOBIL and No-change methods by 12 72%, 23 52% and 54 34% respectively in terms of the time required to complete the simulation scenario  This shows that our method's foresight compensates for its apparent conservativeness arising from the need to preserve passenger comfort 
2) Lateral Displacement: To identify the differences in lane changing behaviors between the four approaches, the relationship between lateral and longitudinal displacements over the course of the simulation is highlighted in the center plot of Fig    In the plot, the lateral displacement of 0 corresponds to the center of lane 0 while the center of each following lane is 3 5m away 
Comparing the performance of the four algorithms, we see SLAS and EA showing relatively similar performances, resulting from proactive decision making  In contrast, since MOBIL only assesses the advantage of switching to the adjacent lanes, it is unable to see the benefit of proactively switching to lane 2  This explains why EA and SLAS start outperforming MOBIL in terms of travel time (left plot) at around the 130 [m] mark for longitudinal displacement 
As for a direct comparison between SLAS and EA , the benefits of having speed advisory system become apparent in this center plot  Due to speed control, SLAS is able to constantly maintain a greater headway (right plot) without having to brake significantly upon getting too close to the lead vehicle  This results in a smooth lateral displacement profile which allows the vehicle to change lanes with minimal jerk (quantitative analysis to follow in Section IV-C) and deliver better overall timing performance (left plot) 
3) Headway: The right plot in Fig  shows the headway maintained by the ego vehicle over the course of the simulation  In accordance with our prior discussion, MOBIL cruises behind the front vehicle, maintaining a relatively low headway until a sufficient space in the adjacent lane is found to perform the lane-change maneuver 
On the other hand, EA and SLAS show a comparable headway trajectory, however, SLAS maintains a greater headway throughout and achieves the maximum headway prior to EA   Quantitatively, SLAS maintains on average 9 43%, 36 57% and 113 17% more headway than the EA , MOBIL and No-change approaches respectively 
This strong performance by SLAS can be attributed to its incorporation of safety guarantees coupled with its consideration for passenger comfort  4) Distance to closest vehicle: Finally, we compare the distance that ego vehicle maintains from the closest vehicle throughout the simulation  On average, SLAS maintains 9 28%, 32 01%, and 22 84% more distance in comparison to EA , MOBIL and No-change approaches respectively 
These numbers are a testament to the strength of our approach resulting from consideration of long planning horizon coupled with speed control 

Monte Carlo Simulations

To demonstrate the long-term performance of the three approaches (SLAS, EA and MOBIL), we run a series of Monte Carlo simulations on scenarios with randomized initial positions (within a range of 8m) and velocities (within    ranges of 8, 5 and 2 m/s assigned to each of the three lanes randomly) of traffic participants 
The result from 50 simulations is presented in Table   In this table, the columns represent the different evaluation metrics, the rows identify the three algorithms, and the values highlighted in green represent the best result with respect to each evaluation metric  The evaluation metrics, going from left to right in the table, are completion time (s), brake (R [−1,0] ), brake jerk (R [−1,0] ), throttle (R [0,1] ), throttle jerk (R [0,1] ), angular acceleration ( • /s 2 ) and angular jerk ( • /s 3 ) 
Apart from completion time, the remaining metrics, based on the commands passed to the vehicular actuators (Fig  ), are used to model passenger comfort  In terms of average performance, SLAS greatly outperforms the other methods when it comes to passenger comfort since it explicitly accounts for comfort in the formulation 
However, it does so at a cost of slightly reduced performance in regards to travel time, when compared to EA , since SLAS tries to strike a balance between minimizing travel time and maximizing passenger comfort  SLAS also secures the lowest standard deviation, for each of the evaluation metrics, when compared to the other methods, which points to the consistency in its long-term performance 

CONCLUSION

We propose a novel behavior planning module for the multi-lane highway maneuvering scenario, that outputs strategic target lane and reference speed commands, and incorporate it with a state-of-the-art motion planning and control framework  We formulate the approach as a receding horizon mixed integer optimization with the goal to minimize travel time while accounting for passenger comfort for a long planning horizon 
In order to reduce the computational overload, we reformulate the problem by replacing integer variables with binary ones and further incorporate various modifications to aid numerical computations  We also carry out a detailed comparative analysis to demonstrate the performance of our approach on the CARLA simulator 
Our future work includes incorporating various delays and uncertainty measures in the perception, localization and prediction modules to evaluate the robustness properties of our approach 

Flooring Constraint

For y ∈ Z and x ∈ R, the constraint y = x can be represented by the following linear constraints: y ≤ x, y + 1 ≥ x + where > 0 accounts for the feasibility tolerance  where M 0 (big-M) and > 0 accounts for numerical errors (chosen to be 0 1 in our implementations) 

Absolute Value Constraint

For ∆s, L s ∈ R, the constraint |∆s| − L s ≥ 0 can be represented as: This can further be generalized, as done in our implementation, to have different forward and rear safety margins as: ∆s ≥ L f s ∨ ∆s ≤ −L r s where L f s and L r s are forward and rear safety margins respectively  This can be represented with the following linear constraints:
s ≥ 0 where M 0 (big-M) and c ∈ {0, 1} is responsible for making a choice between the two constraints 
Context 3: McPherson County (standard abbreviation: MP) is a county located in the U S  state of Kansas   As of the 2020 census, the county population was 30,223  The largest city and county seat is McPherson  The county is named for Civil War General James B  McPherson 

History

Early history

For many millennia, the Great Plains of North America was inhabited by nomadic Native Americans  From the 16th century to 18th century, the Kingdom of France claimed ownership of large parts of North America  In 1762, after the French and Indian War, France secretly ceded New France to Spain, per the Treaty of Fontainebleau  In 1802, Spain returned most of the land to France, but keeping title to about 7,500 square miles 

In 1803, most of the land for modern day Kansas was acquired by the United States from France as part of the 828,000 square mile Louisiana Purchase for 2 83 cents per acre  In 1848, after the Mexican–American War, the Treaty of Guadalupe Hidalgo with Spain brought into the United States all or part of land for ten future states, including southwest Kansas  In 1854, the Kansas Territory was organized, then in 1861 Kansas became the 34th U S  state 

19th century

From the 1820s to 1870s, the Santa Fe Trail passed through, what is now McPherson County  The trail entered the county, east of Canton, then south of Galva, then north of Inman, and west towards Lyons  In 1855, Charles O  Fuller established a ranch adjacent to the Running Turkey Creek Crossing about two miles south and one mile east of Galva  Fuller's Ranch provided accommodations for travelers on the Santa Fe Trail and was probably the first white settlement in McPherson County 

Peketon County was established in 1860, by the passage of a bill by S  N  Wood:  An act to establish Peketon County  Section 1  - That all that territory west of the sixth principal meridian and south of Township 16, in Kansas Territory, be and the same is hereby erected into a county, to be known by the name of Peketon County  On February 17, 1865, Peketon County was abolished, and McPherson County was made a part of Marion County, which extended from the west line of Chase County to the present western boundary of Kansas 

In 1868, Solomon Stephens and L  N  Holmberg were appointed Justices of the Peace—the first officers in what is now McPherson County  The next year (1869) occurred the first election for the township, now the county of McPherson  McPherson was regularly organized as a county in the spring of 1870, a mass meeting being held at Sweadal  Sweadal, the county seat thus selected, was located about one mile and a half southwest of the present site of Lindsborg  In September, however, the County Commissioners resolved to meet at the latter place, McPherson which had already been located some two years 

In April, 1873, a petition was filed for the county seat re-location  It was signed by 483 voters, and a special election was accordingly ordered for June 10  Upon that day, McPherson received 605 votes, New Gottland 325, King City 3 and Lindsborg 1; McPherson's majority over all, 276  In May the McPherson Town Company had offered, as an inducement for the location of the county seat at this point, the free use of rooms for ten years, and the donation of two squares of land on the town site  The offer was accepted the next month, the County Commissioners selecting blocks 56 and 65  Thus the county seat was established at McPherson and has remained since 

As early as 1875, city leaders of Marion held a meeting to consider a branch railroad from Florence  In 1878, Atchison, Topeka and Santa Fe Railway and parties from Marion County and McPherson County chartered the Marion and McPherson Railway Company   In 1879, a branch line was built from Florence to McPherson, in 1880 it was extended to Lyons, in 1881 it was extended to Ellinwood  The line was leased and operated by the Atchison, Topeka and Santa Fe Railway  The line from Florence to Marion, was abandoned in 1968  In 1992, the line from Marion to McPherson was sold to Central Kansas Railway  In 1993, after heavy flood damage, the line from Marion to McPherson was abandoned



ground_truth: He became deputy prime minister and minister of finance.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What are the benefits of using binary variables in the SLAS formulation?

Read the following text and answer briefly.

Context 3:  The original branch line connected Florence, Marion, Canada, Hillsboro, Lehigh, Canton, Galva, McPherson, Conway, Windom, Little River, Mitchell, Lyons, Chase, then connected with the original AT&SF main line at Ellinwood 

In 1887, the Chicago, Kansas and Nebraska Railway extended its main line from Herington to Pratt   This main line connected Herington, Ramona, Tampa, Durham, Waldeck, Canton, Galva, McPherson, Groveland, Inman, Medora, Hutchinson, Whiteside, Partridge, Arlington,  Langdon, Turon, Preston, Natrona, Pratt   In 1888, this main line was extended to Liberal   Later, this line was extended to Tucumcari, New Mexico and Santa Rosa, New Mexico, where it made a connection with the Southern Pacific from El Paso, Texas   The Chicago, Kansas and Nebraska Railway was absorbed by the Chicago, Rock Island and Pacific Railway  This line is also called the "Golden State Route" 

20th century
The National Old Trails Road, also known as the Ocean-to-Ocean Highway, was established in 1912, and was routed through Windom, Conway, McPherson 

Geography

According to the U S  Census Bureau, the county has a total area of , of which  is land and  (0 3%) is water 

Adjacent counties
 Saline County (north)
 Dickinson County (northeast)
 Marion County (east)
 Harvey County (southeast)
 Reno County (southwest)
 Rice County (west)
 Ellsworth County (northwest)

Major highways
  Interstate 135
  U S  Route 56
  U S  Route 81
  K-4
  K-61
  K-153

Demographics

The McPherson Micropolitan Statistical Area includes all of McPherson County 

2000 census
As of the census of 2000, there were 29,554 people, 11,205 households, and 7,966 families residing in the county   The population density was 33 people per square mile (13/km2)   There were 11,830 housing units at an average density of 13 per square mile (5/km2)   The racial makeup of the county was 96 53% White, 0 81% Black or African American, 0 34% Native American, 0 32% Asian, 0 06% Pacific Islander, 0 79% from other races, and 1 16% from two or more races   1 94% of the population were Hispanic or Latino of any race  37 1% were of German, 12 9% Swedish, 12 1% American, 6 7% English and 6 3% Irish ancestry according to Census 2000 

There were 11,205 households, out of which 33 00% had children under the age of 18 living with them, 62 50% were married couples living together, 6 00% had a female householder with no husband present, and 28 90% were non-families  25 50% of all households were made up of individuals, and 11 80% had someone living alone who was 65 years of age or older   The average household size was 2 49 and the average family size was 2 99 

In the county, the population was spread out, with 25



ground_truth: Reduced computational complexity.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What was the population of McPherson County according to the 2020 census?

Read the following text and answer briefly.

Context 3: 40% under the age of 18, 10 30% from 18 to 24, 25 20% from 25 to 44, 21 80% from 45 to 64, and 17 30% who were 65 years of age or older   The median age was 38 years  For every 100 females there were 95 90 males   For every 100 females age 18 and over, there were 92 90 males 

The median income for a household in the county was $41,138, and the median income for a family was $48,243  Males had a median income of $33,530 versus $21,175 for females  The per capita income for the county was $18,921   About 4 20% of families and 6 60% of the population were below the poverty line, including 5 20% of those under age 18 and 8 10% of those age 65 or over 

Government

Presidential elections
McPherson county is often carried by Republican candidates  The last time a Democratic candidate has carried this county was in 1964 by Lyndon B  Johnson 

Laws
Following amendment to the Kansas Constitution in 1986, the county remained a prohibition, or "dry", county until 1996, when voters approved the sale of alcoholic liquor by the individual drink with a 30 percent food sales requirement 

Education

Colleges
 McPherson College in McPherson
 Bethany College in Lindsborg
 Central Christian College in McPherson

Unified school districts
 Smoky Valley USD 400
 McPherson USD 418
 Canton-Galva USD 419
 Moundridge USD 423
 Inman USD 448

School district office in neighboring county
 Goessel USD 411
 Little River-Windom USD 444

Museums
 Birger Sandzén Memorial Gallery in Lindsborg
 McCormick-Deering Days Museum in Inman
 McPherson Museum in McPherson
 Lindsborg Old Mill & Swedish Heritage Museum in Lindsborg
 Kansas Motorcycle Museum in Marquette

Communities

Cities

 Canton
 Galva
 Inman
 Lindsborg
 Marquette
 McPherson (county seat) 
 Moundridge
 Windom

Unincorporated communities
† means a Census-Designated Place (CDP) by the United States Census Bureau 
 Conway
 Elyria†
 Groveland
 Johnstown
 New Gottland
 Roxbury†

Ghost towns
 Alta Mills
 Battle Hill
 Christian
 Doles Park
 Elivon
 King City
 Sweadal

Townships
McPherson County is divided into twenty-five townships   The cities of Lindsborg and McPherson are considered governmentally independent and are excluded from the census figures for the townships   In the following table, the population center is the largest city (or cities) included in that township's population total, if it is of a significant size 

See also
 List of people from McPherson County, Kansas
 National Register of Historic Places listings in McPherson County, Kansas
 McPherson Valley Wetlands
 Maxwell Wildlife Refuge

References

Notes

Further reading

 Wheeler, Wayne Leland  "An Analysis of Social Change in a Swedish-Immigrant Community: The Case of Lindsborg, Kansas " (PhD dissertation, University of Missouri-Columbia; ProQuest Dissertations Publishing, 1959  5905657) 

County
 Through the Years: A Pictorial History of McPherson County; McPherson Sentinel' Heritage House Publishing Co; 1992 
 McPherson County First Courthouse Built About 1869 or 1870; Lindsborg News-Record; March 30, 1959 
 Pioneer Life and Lore of McPherson County, Kansas; Edna Nyquist; Democratic-Opinion Press; 1932 
 A History of the Church of the Brethren in Kansas (includes McPherson College history); Elmer LeRoy Craik; McPherson Daily; Republican Press; 397 pages; 1922 
 Portrait and Biographical Record of Dickinson, Saline, McPherson, and Marion Counties, Kansas; Chapman Bros; 614 pages; 1893 
 Standard Atlas of McPherson County, Kansas; Geo  A  Ogle & Co; 82 pages; 1921 
 Plat Book of McPherson County, Kansas; North West Publishing Co; 50 pages; 1903 
 Edwards' Atlas of McPherson County, Kansas; John P  Edwards; 51 pages; 1884 

Trails
 The Story of the Marking of the Santa Fe Trail by the Daughters of the American Revolution in Kansas and the State of Kansas; Almira Cordry; Crane Co; 164 pages; 1915  (Download 4MB PDF eBook)
 The National Old Trails Road To Southern California, Part 1 (LA to KC); Automobile Club Of Southern California; 64 pages; 1916  (Download 6 8MB PDF eBook)

Mennonite Settlements
 Impact of Mennonite settlement on the cultural landscape of Kansas; Brenda Martin; Kansas State University; 1985/1988  
 Mennonite settlement : the relationship between the physical and cultural environment; Susan Movle; University of Utah; 1975/1886 
 Status of Mennonite women in Kansas in their church and home relationships; Eva Harshbarger; Bluffton College; 1925/1945 

External links

County
 
 McPherson County - Directory of Public Officials
Historical
 , from Hatteberg's People'' on KAKE TV news
Maps
 McPherson County Maps: Current, Historic, KDOT
 Kansas Highway Maps: Current, Historic, KDOT
 Kansas Railroad Maps: Current, 1996, 1915, KDOT and Kansas Historical Society

 
Kansas counties
1867 establishments in Kansas
Populated places established in 1867



ground_truth: 30,223.